Methods and systems for collecting driving information and classifying drivers and self-driving systems

ABSTRACT

Systems and methods for efficiently addressing technical and privacy/authorization obstacles associated with tracking of individuals in a vehicle, and enabling route-based analysis to determine driving behavior, socio-demographics, future profitability, and interests of individuals or self-driving systems. Driving information is collected using a device associated with a driver and a vehicle or using data collected by systems of self-driving vehicles. The frequency and methods used for the collection of driving information can be modified based on location and movement of the device and based on previous classification of the driver or self-driving system, thereby enabling efficient use of bandwidth and battery and increasing accuracy of the classification. The driving information is encoded and transmitted to a server, where future typical route segments that the driver is likely to travel are predicted, and the driver, or the self-driving system, is classified into one or more groups based on the encoded driving information.

RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.15/593,785, filed May 12, 2017, which is a continuation-in-part of U.S.application Ser. No. 15/366,179, filed Dec. 1, 2016, now abandoned,which is a divisional of U.S. application Ser. No. 14/229,821, filedMar. 28, 2014, now abandoned. The entire teachings of the aboveapplications are incorporated herein by reference.

BACKGROUND

A huge ecosystem has been developed around the use of cookies on theInternet to track where people go online and the web sites visited byindividuals, but a similar system to track the places visited by peoplein the physical world and the routes they travel to reach theirdestinations has yet to be developed. Online, the use of cookies hasbeen widely recognized as critical to allow content providers tomonetize more efficiently their web sites by delivering content,promotional offers, and advertising tailored to the demographics,potential profitability, and general profile of an individual asdetermined by the history of all web pages visited and clicked on,search history, and a large set of data derived from online behavior.Online, it is now very easy to know which sites have been visited andthe browsing patterns and behavior and interest profile of most people.In the physical world, however, this capability is not as developed, andan implicit and indirect consumer benefit of using something likephysical world tracking cookies has not been introduced. Location basedtechnologies (e.g., GPS or network based location technologies derivedfrom satellite, cellular, or Wi-Fi beacons) have allowed the delivery ofcontent based on approximate (e.g., state/zip code) or precise location(e.g., GPS within 10-50 meters). Although location technologies providemuch information for the delivery of various services, as well ascontent and advertising, real-time punctual location information stilllacks additional data elements that could unlock rich new analyticalcapabilities that determine behavioral patterns and demographics. In thephysical world (as in the online world) it is not enough to know wheresomeone is, but one needs to know where that person has been before andwhere he or she is heading, the time of day, the frequency of routestravelled, and other factors such as correlation withdemographics/profiles obtained online. Similarly, the ability to predictthe profitability of those individuals for several products and servicesis dependent on knowing key components of their routes driven and theirdriving patterns in the physical world, such as, for example, speed,distance travelled, accumulated time and mileage driving, andneighborhood and malls visited. One possible solution could be toconstantly track everyone (e.g., through their mobile devices), howeverthe current state of the art has significant limitation as continuoustracking creates both technical obstacles (e.g., amount of datacollected, wireless bandwidth utilization, and battery life) andprivacy/authorization issues.

SUMMARY OF THE INVENTION

The present disclosure describes systems and methods to efficientlyaddress obstacles such as technical and privacy/authorization issues,and release the rich potential of route-based analysis to determinebehavior, demographics, interests, and predicting profit potential forcertain products and services. The profit predictive capability can beapplied to several industries, and in one embodiment, a clearinghouseexchange system can classify drivers into profitability classes and caninterface various service providers with a feedback loop that providesactual profitability for products and services over time of the driversin order to adjust the predictive algorithms of the clearinghouseexchange system through a self-learning system.

There is significant benefit provided by the methods and systems thatprovide a profit potential classification of a driver or a fleet ofself-driving vehicles based on the routes driven or based on aprediction of the routes driven. For purposes of illustration andwithout limitation, wireless carriers can make outsized profits frompeople who do not drive much, and people who pay a monthly fee for datapackages they do not use because they are mostly homebound and mostlyuse Wi-Fi hotspots at home for their data intensive applications. Today,state of the art methods and systems used to understand the amount ofdriving done by individuals is rudimentary and there are no centralizedclearinghouse exchanges to provide information to that effect toproviders of products and services.

There is significant benefit provided by methods and systems thatprovide an interest profile classification of a driver or a fleet ofself-driving vehicles based on the routes driven or based on aprediction of the routes driven. For purposes of illustration andwithout limitation, content, advertisers, and ad networks would benefitfrom having the capability to target drivers that often go to medicalcenters and malls on afternoons during weekdays as this may, forexample, indicate an older retired target segment. Furthermore, there issignificant benefit provided by methods and systems that can reliablydeliver promotional offers along certain routes while those promotionaloffers are still actionable. For purposes of illustration and withoutlimitation, a merchant would have the ability deliver a promotionaloffer to a driver knowing that the particular driver travels regularlyalong a route that can be slightly modified with minimal additionaldrive time to bring the driver to the merchant's establishment, and thedelivery of such an offer can be timed in such a way that it occursprior to the driver passing a specific exit or junction point.

There is significant benefit provided by methods and systems thatprovide a convenience or a benefit or reward to individuals to allowthem to be tracked when in a vehicle and doing so at minimal cost interms of cost to the individuals and in terms of additionalbandwidth/data and battery utilization. For purposes of illustration andwithout limitation, by leveraging specific methods and systems toautomate the start and end time of the tracking so that it coincideswith the time a user of a mobile device is in a moving vehicle,significant bandwidth and battery savings can be achieved. Similarly,limited tracking is required in some cases between two roadintersections as the vehicle is assumed to follow a known path that hasalready been geocoded by map providers. Furthermore, the disclosedmethods and systems can minimize the amount of data transmitted to thestrict minimum required to, for example, reconstruct routes driven,mileage, speed, and driving behavior.

One example embodiment is a method of collecting driving information andclassifying drivers, including human drivers or self-driving systems.The method includes collecting driving information using a deviceassociated with a driver and a vehicle, where the driving informationcan include routes driven in the vehicle, geocoded locations, mileage,times of day, and speeds. The collection of the driving information isenabled and disabled based on location and movement of the device. Themethod further includes (i) encoding the driving information andtransmitting the encoded driving information to a server, (ii) storing,in a database associated with the server, an identifier associated withthe driver and the encoded driving information, and (iii) determining,and storing in the database, predicted future typical route segmentsthat the driver is likely to travel over a certain period of time andassociated times of day based on the encoded driving information, and(iv) classifying the driver into one or more groups based on the encodeddriving information.

Another example embodiment is a system for collecting drivinginformation and classifying drivers. The system includes a device,database, and processor. The device is associated with a driver and avehicle and (i) collects driving information, where the drivinginformation can include routes driven by the driver in the vehicle,geocoded locations, mileage, times of day, and speeds, (ii) enables anddisables the collection of driving information based on location andmovement of the device, (iii) encodes the driving information, and (iv)transmits the encoded driving information to a server. The database isassociated with the server and is in communication with the device. Thedatabase stores an identifier associated with the driver, the encodeddriving information, and predicted future typical route segments thatthe driver is likely to travel over a certain period of time andassociated times of day based on the encoded driving information. Theprocessor is associated with the server and is in communication with thedatabase and the device. The processor determines the predicted futuretypical route segments and classifies the driver into one or more groupsbased on the encoded driving information.

Bandwidth used by the device for collecting the driving information orthe frequency of collection may be adjusted based on the classificationof the driver. In some embodiments, an advertisement or promotionalcontent may be associated with at least one of the typical routesegments driven by the driver in the vehicle. A point along theassociated route segment may be determined at which the advertisement orpromotional content is to be presented to the driver, the point alongthe associated route segment providing a sufficient amount of time forthe driver to take an action associated with the advertisement orpromotional content. The advertisement or promotional content is thenpresented to the driver via the device when the driver drives along theassociated route segment in the vehicle and reaches the point along theassociated route segment at which the advertisement or promotionalcontent is to be presented. The point along the route segment may be,for example, a certain distance before an exit or turn leading to anestablishment associated with the advertisement or promotional content,where the certain distance is determined based on vehicle speed andlocation of vehicle along the route segment. In such embodiments,representations of typical route segments driven by specific targetgroups of drivers can be provided to an advertisement provider, and anadvertisement request may be received from the advertisement providerwith a selection of at least one of the route segments driven by thespecific target groups of drivers to be associated with an advertisementor promotional content. When an advertisement or promotional content ispresented to a driver in some embodiments, the driver may be able torespond to the advertisement or promotional content, and the driver canthen be provided a share of advertising revenue for responding to theadvertisement or promotional content. In some embodiments, the drivercan be presented with an incentive to visit an establishment associatedwith at least one of the typical route segments if the driver repeatedlytravels along the route segment. In such an embodiment, the presence ofthe driver can be detected around the location of the establishment fora sufficient amount of time to determine whether a visit to theestablishment occurred to determine whether to allow a redemption of anypromotional incentive provided by the establishment, and an account ofthe driver can be credited with the incentive if the driver visits theestablishment and fulfills any requirements of the incentive.

The device may be a mobile computing device, in which case a profile ofthe driver can be created based on the driver's use of the mobile deviceand the encoded driving information. Socio-demographic or potentialinterests for products and services can be associated with the profileof the driver based on the driving information, and, based on thesocio-demographics and interests, the driver can be associated withcorresponding advertising categories used by online advertiser networksand exchanges. The driver's activity on the mobile device and the routesdriven by the driver and locations visited can be synchronized with thedriver's virtual activity on internet browsers to represent, in theprofile of the driver, web sites visited using the browser and physicalestablishments visited or driven by that are associated with the typicalroute segments to categorize the driver in an advertising category to beused by advertisement providers.

In some embodiments, classifying the driver into one or more groups caninclude classifying a group of drivers or a fleet of self-driven orpartially self-driven vehicles that use materially similar underlyingself-driving technology component software, hardware, or sensorconfiguration. In such embodiments, collecting driving informationincludes collecting driving information using a device associated witheach driver and associated vehicle or using connectivity to processorsand systems associated with a self-driven or partially self-drivenvehicle, where the driving information can include mileage, routesdriven by the vehicle, time of day, and speed. In such embodiments,classifying the driver into one or more groups based on the encodeddriving information includes determining an insurance risk (or lossratio from general expected liability or property claims) for eachdriver or, in a case of a fleet of self-driven or partially self-drivenvehicles, determining an insurance risk by aggregating the encodeddriving information of all vehicles of the fleet that uses similarunderlying self-driving technology components. In such a case, theinsurance risk determination can include applying (i) a plurality offeatures of the routes driven including, for example, mileage(cumulative or per time period), speed (as compared to speed limit foreach road segment), types of roads driven (e.g., highway versus city),types of areas driven through (high risk areas with high historicalclaims), or zip codes, and (ii) a plurality of features of the driverincluding, for example, historical routes driven, driving behavior,times of day, age, gender, zip code, credit score, type of car driven,and historical infractions or, in a case of a fleet of self-driven orpartially self-driven vehicles, the underlying driving technologyincluding sensors sensitivity, historical cumulative mileage, areas thefleet is being driven, speed at which the fleet is being driven, andpercentage of time the vehicles are being self-driven and associatedclaims across the fleet, to assigned weights for scaling the risk. Thedriver or underlying driving technology insurance risk classificationmay be presented to a plurality of entities, requests may be receivedfrom at least one of the entities to send promotional messages todrivers within a specific risk classification or provide insurance foror offer services to the driver or fleet of self-driven vehicles usingthe underlying driving technology, and at least one of the entities maybe selected to provide insurance for or offer services to the driver orfleet of self-driven vehicles using the underlying driving technology.Determining an insurance risk for a plurality of self-driven vehiclescan include determining an insurance risk based on a combined mileagefor the plurality of self-driven vehicles and a combination of routesdriven by the plurality of self-driven vehicles. Further, weightsaffecting the plurality of factors used to determine the classificationof insurance risks may be modified in response to actual loss and claimsdata being provided to cause the determined insurance risk, when appliedretroactively to historical route and driver data, to correspond moreclosely to an actual insurance risk as calculated from actual loss andclaims data provided over time.

In some embodiments, the driving information can be matched with routesegments known to be associated with self-driving vehicles. The deviceand driver identifier can be associated with a self-driving capablevehicle status, and time periods, mileage, and route segments duringwhich the vehicle is driven in self-driven mode or driven manually bythe driver may be identified. The driving information can be allocatedaccording to the identified time periods, mileage, and route segments toaffect the classification of the driver or self-driving system.

In some embodiments, classifying the driver into one or more groups caninclude classifying the driver according to a predicted profitabilitypotential for certain products or services or according to interests,preferences, behavior profile, and socio-demographics of the driverbased on the encoded driving information.

In some embodiments, classifying the driver into one or more groups caninclude classifying a group of drivers using partially self-drivenvehicles for the purpose of predicting an insurance risk according toinformation not made available to third parties by the providers ofself-driven systems and self-driven vehicles such as, for example, (i)who the driver is, (ii) when the vehicle is not driven in self-drivenmode (manually driven), (iii) the percentage of time the vehicle is inself-driving mode, (iv) the type of route segments being driven while inself-driving mode, (v) the speed and mileage driven while inself-driving mode, and (vi) the density or percentage of surroundingvehicles on the routes driven that are in a self-driving mode. In suchembodiments, the classification can be achieved by matching a specificdriver to a self-driven vehicle by matching the path of the driver'smobile device to the path of the self-driven vehicle as determined bythe providers of the self-driven systems and self-driven vehicles. Thedetermination of when and where the vehicle is in self-driving mode canfurther be achieved by matching the typical driving pattern or“signature” of such self-driven vehicle with the encoded drivinginformation. The matching process can include comparing the encodeddriving information captured from the device of the driver with (i)typical speed of self-driven vehicles over specific route segments and(ii) typical acceleration and deceleration patterns of self-drivenvehicles in the context of traffic lights or changing lanes. Once thedriving pattern matching is completed, the extent to which the vehicleis being self-driven can be determined and the corresponding encodeddriving information can be associated to the underlying drivingtechnology, and the extent to which the vehicle is driven manually canbe determined and the corresponding encoded driving information isassociated to the proper driver identifier.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing will be apparent from the following more particulardescription of example embodiments of the invention, as illustrated inthe accompanying drawings in which like reference characters refer tothe same parts throughout the different views. The drawings are notnecessarily to scale, emphasis instead being placed upon illustratingembodiments of the present invention.

FIG. 1 is a block diagram illustrating an example mobile deviceassociated with a driver and that collects driving information,according to an example embodiment of the invention.

FIG. 2 is a flow diagram illustrating determining whether the user of amobile device is driving a vehicle, according to an example embodimentof the invention.

FIG. 3 is a schematic diagram illustrating a system for collectingdriving information and classifying drivers, according to an exampleembodiment of the invention.

FIGS. 4A and 4B are schematic diagrams illustrating minimizing batteryand bandwidth utilization and minimizing an amount of data transmittedwirelessly by a mobile device, according to an example embodiment of theinvention.

FIG. 5 is a flow diagram illustrating collecting driving information andclassifying drivers, according to an example embodiment of theinvention.

FIG. 6A is a flow diagram illustrating presenting an advertisement orpromotional content to a driver based on collected driving information,according to an example embodiment of the invention.

FIG. 6B is a flow diagram illustrating determining insurance risk for adriver or fleet of fully or partially self-driven, according to anexample embodiment of the invention.

FIG. 7 is a schematic diagram illustrating using known routes driven bya driver to deliver an advertisement to the driver and compensate thedriver for responding to the advertisement, according to an exampleembodiment of the invention.

FIG. 8 is a schematic diagram illustrating using known routes driven bya driver to predict profitability of the driver, according to an exampleembodiment of the invention.

FIG. 9 is a schematic diagram illustrating creating profiles of driversbased on collected driving information.

FIG. 10 is a schematic diagram illustrating assigning a profitabilityindex to a group of drivers or self-driven vehicles.

DETAILED DESCRIPTION OF THE INVENTION

A description of example embodiments of the invention follows.

The disclosed systems and methods can be used to manage driverprofitability profiles for certain products and services and exchange ofinformation with cloud-based systems. The methods and systems cangenerate information and profiles associated with routes driven orexpected to be driven, targeted content delivery in anticipation ofroutes driven, and predict demographics and profitability of drivers orfleets of self-driven cars for specific products and services. Thedisclosed methods and systems can be used by various intermediaries,sales agents, brokers, ad networks, and service providers to determineand classify the potential profitability of certain individuals orgroups of individuals or fleets of vehicles for specific products andservice based on prior historical routes driven and to deliver to thoseindividuals content or promotional offers and information tailored tothose individuals' interests as calculated and determined based on thedisclosed systems and methods that provide historical and predictedroutes that those individuals have driven or are expected to drive usingtraditional vehicles, semi-autonomous vehicles, or fully self-drivenvehicles.

An example system can include three parts. First, a software programresiding on a mobile device containing instructions for the local mobiledevice CPU. The mobile device can convert, using the disclosed methods,a vast amount of location data points into a compressed manageablerelevant metadata, such as, for example, routes or road segments,mileage, speed patterns, and time of day, to minimize data and bandwidthutilization. To further minimize data and battery usage, the software onthe mobile device can operate in the background as a service to measurevarious on-board sensors and cell-ID changes in order to determinewhether the device owner is in a vehicle and whether to initiate GPSmeasurements, which are more resource intensive. This enables the deviceto avoid collecting data if not in a vehicle to reduce battery usagefrom computation-intensive operations that are only needed while movingin a vehicle (e.g., there is no need to constantly calculate speed whenthe device is determined to not be in a moving vehicle).

Second, a program residing on a centralized processor can access acentralized database of driver profiles and compute various parametersof the driver behavior and store the routes driven, as well as variousdata elements that can be used to predict future routes based onhistorical driving habits. The central CPU can also determine estimatesof the potential profitability of the driver for various products andservices based on driving patterns, mileage, time on the road, speed,and other external factors, such as, for example, safety indices ofroads travelled based on time of day, speed limits of road segmentstravelled, demographics (e.g., average income, home ownership) andzoning (e.g., residential, commercial, malls, industrial) of the areastravelled through, and the dominant general characteristics ofdestination points (e.g., malls, residential areas, office areas,medical centers, entertainment zone). A software module running on thecentral CPU can correlate various driving patterns with demographics andinterests categories. For purposes of illustration and withoutlimitation, a driver that travels on a regular basis to entertainmentareas of a city late on Saturday nights can be categorized as a single,young individual or a recently married person without children. Thisdemographic categorization can be taken into consideration whendetermining the profitability for various products and services. Forexample, a person categorized as young and single may be considered ahigh user of wireless data service or a subscriber to music servicesthat are streamed on a costly and bandwidth-limited external wirelessnetwork. A feedback loop may provide over time the actual profitabilityof the drivers once they do purchase specific products or services. Theactual results can then be stored in a centralized database, andprofitability classification algorithm parameters can be fine-tuned andmodified so that the profitability estimates converge towards the actualresults.

The third part of the example system relates to creating a derivedbenefit to the drivers from background tracking. As with online cookies,the derived benefit requires a combination of convenience elements forthe driver through the delivery of tailored content, promotional offers,advertising tailored based on captured route information and historicaldriving patterns, as well as encouraging drivers (e.g., using cash-backincentives) to allow themselves to be tracked by a merchant (e.g.,insurance agent) without necessarily receiving discounts from themerchant or related entity (e.g., without necessarily receiving adiscount from the insurance carrier selected by the insurance agent). Toachieve this goal, all or a portion of the ad revenue from an always-onfirst party application on the device can be paid into a stored valueaccount to the benefit of the driver. A sub-system of this component canprovide advertising networks and ad exchanges route and destination data(such as, for example, most-travelled routes and corresponding time ofday, most frequent destination malls or stores and corresponding time ofday) in a format that can be used by advertisers and linked to theadvertising identifiers of the mobile device. With this data, adnetworks can enhance targeting capabilities of their platforms for thebenefit of their advertisers. In addition, through data matching andsynching between mobile advertising identifiers and desktop browserbased third party cookies, the targeting capability that the ad networkscan offer to advertisers can be extended and unified across both desktopbrowsers and third-party applications on mobile devices they serve, thusextending the capabilities of third-party cookies from tracking websites visited to physical location visited.

The disclosed methods and systems provide a number of advantagesdepending on the particular aspect, embodiment, and/or configuration.One advantage includes providing driver profiles based on routes driven,which is information that has not been available before. The profile canbe used in several ways, including the calculation of profit and riskpotential of the driver for a wide variety of products and services. Theprofile can add new dimensions to the typical demographics categoriesobtained through other means. For example a “65 plus” year old or“retired” category can be nuanced by “stay at home” if no significanttravelling is detected, “socially active” if several routes inresidential areas is detected, or “traveler” if several road tripsoutside of the city is detected. This ability to amplify significantlythe potential demographics of an individual using the patterns of theroutes driven has tremendous value to several industries, as would beunderstood by those familiar in the art of inferred demographics todeliver targeted content, promotional offers, or advertising for thirdparties that may acquire access to those databases or when used inconjunction with ad networks if those demographics can be correlated toonline traditional cookies from major ad networks or publishers.

Another advantage includes a system that can track route information ina way that is both battery efficient and bandwidth efficient in terms ofreduction of the amount of data transmitted wirelessly on networks andcost in terms of network capacity. With GPS location being a highbattery draining service, the system limits high accuracy locationcalculations to a minimum required to determine with the required levelof accuracy for main parameters (e.g., mileage, average speed onsegments, time on the road, and characteristics of destination points)that are required for the determination of the demographic profile andfor the calculation of the predicted profitability for various productsand services.

Yet another advantage is the ability to monitor fleets of self-drivencars or semi-autonomous cars and determine parameters for thecalculation of predicted profitability for various products and servicesthat the manufacturer or designer of the self-driven software and themanufacturers or designers of the n-vehicle sensors (e.g., cameras andproximity sensors) needed to deploy their fleet of self-driven cars,such as, for example, total mileage from the entire fleet, average speedon highways of the entire fleet, percentage of human override time ofthe entire fleet and put this information in an exchange andclearinghouse database accessible by various providers of products andservices for those fleets such as insurance companies that may want somehistorical view on the performance of a particular self-driven softwareversion and sensor configuration as it relates to road accidents andimpact on liability insurance costs especially as it compares to theperformance of alternative self-driven software and sensor configurationfrom another manufacturer or from the same manufacturer in order tobetter determine predicted future insurance costs and rate insuranceservices to fleets of self-driven vehicles using similar underlyingtechnologies more accurately.

Further advantages include the build-up of a centralized database of keyprofitability parameters for several products and services outside ofthe ownership of the providers of such products and services so that thedatabase can be leveraged across all players in a specific industry.Such a database can be viewed as an industry utility organization forthe purpose of better targeting profitable segments of a particularmarket or declining high risk individuals. It can also force someproviders to disclose information that has an impact on risks in astandardized way. For example, manufacturers of self-driven vehicles oroperators of self-driven fleets may disclose a very high total mileagedriven as well as a very low total cost from accident and liabilityclaims; however they may not disclose that the vehicles were mostlyself-driven on highways where it is easy to show high mileage with lowclaims, even with substandard self-driving technology. The availabilityof an industry-wide exchange and repository of route data would preventsuch partial disclosure of information. The open architecture of thesystem allows wireless service providers, cable TV providers, medicalproducts and service providers, ad networks, or insurance companies toconnect and interface to the database to complement their perspective ofa particular driver or fleets of self-driven vehicles with routes driveninformation and inferred demographics and profitability.

FIG. 1 is a block diagram illustrating an example mobile deviceassociated with a specific driver that has stored in its device storage109 code that embodies the functionality of a method for collectingdriving information. FIG. 1 also illustrates a high-level architectureof an analytics module/agent 104 assisting data flow between a devicestorage 109, the device's local processor CPU 102, and an analyticscentralized server 150, and device systems including device radios 105,device GPS 107, and device local sensors 103. The analytics agent 104receives/collects data from the device systems that may be stored on thedevice storage 109 and a local application device database 101 or may betransmitted back to the analytics server 150 based on an algorithm thatmaximizes battery life and minimized amount of data transferred. Theanalytics agent 104 also provides a feedback mechanism by which datacollection frequency in the device can be tuned. The analytics agent 104has a background activity that collects those observation data atintervals of time that are determined by algorithms that are optimizedto gather as many data points required for the inference pipeline thatdetermines with high-level probability in-vehicle movement with theleast impact on the device resources. This data collection activity canruns as a background thread on the device and does not block the normalfunctioning of the device. The software code residing in device storage109 can cause the device processor 102 to periodically make measurementsusing various local sensors 103 using appropriate APIs to determine ifthe device (and its user) are in a moving vehicle. The measurementssequence requested by the instructions in the software may be such thatit minimizes the use of location measurements that drain the battery orminimizes transmitted data over Wi-Fi or mobile networks.

The disclosed methods and systems may use all location methods availableon the mobile device including, but not limited to, measurement celltower ID and transmit/received power, location information provided bythe device itself (based on services provided by the device itself suchas GSP, Assisted GPS method, Standalone GPS method, Cell ID method,enhanced cell ID method using known sectorized cells IDs, the timingadvance (e.g., difference between its transmit and receive time) andestimated timing and power of the detected neighbor cell (e.g., Wi-Fitriangulation methods using know local Wi-Fi hotspots) as well asaccelerometer sensors of the device. The frequency of those measurementsand the types of measurements made (some of which are more battery/CPUand data intensive than others) can be determined by the softwareresiding on the storage memory 109 of the device. The cell ID, beingavailable in most cases at little or no battery/bandwidth cost since itis used during the normal operation of a mobile device, can be usedalong with regular full location measurements, the frequency of whichcan be determined by the algorithm itself. Mobile devices typically areserved by several specific cell towers nearby based on powermeasurements made by the device. Similarly, the device usually senseseither a home or work Wi-Fi network (even when not communicating withthose networks). The set of cell towers sectors ID and the broadcastSSID may be stored in a local database 101 along with a time stamp whenthe device starts and stops detecting those specific sector ID and Wi-FiSSIDs—The program causes to assign a “known stationary location” on theapplication database 101 when it detects that the same ID information isrepeatedly captured, which would indicate that the driver has reached aregular route start point (such as home) or regular route destination(such as work). This could be, for example, a home Wi-Fi SSID, the cellsector of a coffee shop visited regularly, or the Wi-Fi of a relative orfriend where the driver spends a significant amount of time. Because ofthe small coverage area of Wi-Fi hotspots compared to cell ID, thedevice analytics module and agent 104 can collect data from othersensors' measurements when it detects a loss of connection with a knownstationary location Wi-Fi SSID. At that point, the algorithm can triggermore-precise location measurements (such as frequent and large change inlinear acceleration movement or changes in angular rotational velocity,or sustained changes in directions as measured by the accelerometer,gyroscope, and magnetometer sensors 103) to determine whether the deviceis in motion. Once the probability of in-vehicle movement is high enoughfor GPS measurements, data collection starts at a much higher frequencyand an in-vehicle status is sent to the analytics centralized server150. The threshold for speed and acceleration for an in-vehicledetermination (and corresponding switch to a status of more constanttracking of location and speed using GPS and other location services) isimportant for reducing the bandwidth and battery drain on the device.Similarly, a deceleration and a stop in movement may cause the device toreturn to a state of reduced tracking intensity after a certain periodof time of reduced movement. In one embodiment, in-vehicle status may beset directly by the user or by in-vehicle transmitters (e.g., Bluetooth,RFID) that connect to a corresponding radio 105 and in-vehicle presenceof the mobile device and its owner.

FIG. 2 is a flow diagram illustrating whether the user of a device isdriving a vehicle. If it is determined that the device owner is in amoving vehicle (2-01), a message may appear on the device asking if theuser of the application is driving his own vehicle or if he/she ismoving in a taxi, a friend's car, a public self-driven car, a bus, atrain, or a bicycle. If the message is ignored an attempt would be madeto pair the device with the owner's vehicle known internal beacons suchas Bluetooth or RFID (2-02). If the pairing is not successful (e.g.,because such beacon is not available or because phone settings are notallowing such pairing), then in some embodiments it may still beimportant to determine if the driver is driving his own car or is apassenger in someone else's car or a passenger in public transportation(e.g., train, taxi, bus, or shared ride on self-driven car). It isbeneficial to determine if the owner of the mobile device is driving hisown vehicle without the need for costly pairing technology in thevehicle. Linking the owner of the device to a specific vehicle and tospecific routes can provide valuable data to determine interests andprofitability for certain products and service. For example, in order tocalculate the profitability of the driver for auto insurance, it wouldbe important to ignore mileage from routes travelled in a bus, whereasto calculate the profitability of a driver for wireless connectivity, itwould be important to actually know if a long route is made on a bus ora public self-driven shared ride as the probability that the owner ofthe mobile device uses his or her phone for data intensive applicationswhile sitting in a bus or in a self-driven shared ride is quite high,thus reducing the overall profitability calculations for that particularservice as calculated by the central server.

To differentiate between different scenarios, the last known parkinglocation may be determined from the end points of prior routes (2-00).This determination is done by looking at the end point of the priorroute giving higher confidence to the home and work locations (e.g.,home being where a specific device stays overnight several times in arow stationary or where it gets connected overnight to a specific Wi-Finetwork, and work being where the same event occur but during weekdaysmornings or afternoons). The start point of the route is compared withthe last known parking location of the vehicle (end point of priorroute) (2-03). One option is to verify if the start point of the newroute is within a specific distance form the last known parking locationof the usual vehicle used. If the distance is above a specificthreshold, it may be assumed that the probability of using publictransportation or a ride with a friend in different vehicle issignificant and the mileage of the new route can be categorized as such.The last location of a route is, thus, important to associate a specificvehicle to a specific driver, and the last location of a vehicle isexpected to be the start point of the next trip.

Even if the distance between the last known parking location and thestart of the new route is below a specific threshold it still cannot beassumed with enough certainty that the driver is not using his/her ownvehicle. The following determinations may be made once the new route iscompleted on the centralized server. Several retroactive analyticaldeterminations may be made retroactively to determine if the route wascompleted using the device owner's vehicle or through other modes oftransportation. At retroactive comparison step (2-05), the systemcompares the new route with the known routes and stations of surroundingtrains and buses, and if the route and the stops match within a specificconfidence factor with known public transportation routes, the new routewill be categorized as such for the profiling and profit determinationalgorithms (2-04). If no public transportation route matching isachieved, a subsequent step (2-06) considers both driving pattern andwhether subsequent start points of the following routes are determinedto imply the use of a different vehicle. In 2-06 a, driving pattern istested against usual speed (unusually high or low average speed comparedto past measured behavior indicate a different driver) and unusualroutes: capturing several route segments for the same driver show apattern of repeat preferred routes routinely taken by the driver andvehicle so that some routes (e.g., a drive to the mall) are associatedwith a confidence score being of a particular level of being driven bythe same driver. If the route to a specific usual destination does notmeet that confidence threshold, the assumption will be that the driverof the vehicle is different (e.g., a friend, another family member, ataxi driver, or a self-driven car) (2-08). In 2-06b, the post processingmay be done not only on the new route but also on subsequent routes. Ifa new route segment AB is initiated “close” (distance beyond a certainthreshold) from the last known parked location A, temporarily concludethat the driver is in the vehicle and trace new route segments AB. Butif the assumed last parked location of this new route segment point B isfar from the next departure point on the next route segment (point C),and this occurs potentially a multitude of times (end point D of routeCD is far from start point E of segment EF), and the last known endpoint location point F is very close to one of the last known parkedlocations in the system (point A) then all the intermediate roadsegments (AB, CD, and EF) can be removed and not associated with thevehicle as it is assumed the person was, for example, in a taxi orriding with a friend in a different car.

FIG. 3 is a schematic diagram illustrating a system for collectingdriving information and classifying drivers. Various connections betweenelements are described in the following description. These connectionsare general and, unless specified otherwise, may be for example director indirect, wired or wireless, and this specification is not intendedto be limiting in this respect. In the description of variousillustrative embodiments, reference is made to the accompanyingdrawings, which form a part hereof, and in which is shown, by way ofillustration, various embodiments in which aspects of the disclosure maybe practiced. It is to be understood that other embodiments may beutilized and structural and functional modifications may be made,without departing from the scope of the present disclosure. The logicaloperations of the various embodiments of the disclosure described hereinare implemented as: (1) a sequence of computer implemented steps,operations, or procedures running on a programmable circuit within acomputer, and/or (2) a sequence of computer implemented steps,operations, or procedures running on a programmable circuit within adirectory system, database, or compiler.

In FIG. 3, an interactive system 300 is shown for use in connection withthe classification of interests and profit potential for variousproducts and an action-less points delivery system to encourage trackingpermissions, according to an example embodiment of the presentdisclosure. The interactive system 300 provides points that drivers canconvert to cash, determines interest attributes and profile of driversbased on historical routes driven and calculates dynamically profitpotential of certain drivers or fleets of self-driving vehicles forproducts, such as auto insurance, life insurance, wireless services,medical products, etc. The points are provided to drivers simply fordriving the roads they already drive, so there is no specific actionrequested from the driver in order to receive such cash convertiblepoints. In one embodiment, the interactive system 300 can also classifydrivers into groups or risk pools based on loss ratio prediction for caror vehicle insurance as predicted by the routes driven and other datacaptured by the system. The classification is dynamic in that it canchange over time as additional data is captured dynamically andprocessed by the interactive system 300.

The system 300 includes a centralized system server, shown as vehicleportal server 307 a. The computing device 307 a may include one or moreprocessors, which may execute instructions of a computer program toperform any of the features described herein. The instructions may bestored in any type of computer-readable medium or memory, to configurethe operation of the processor(s). For example, instructions may bestored in one or more of a read-only memory (ROM) , random access memory(RAM), internal or removable media , such as a Universal Serial Bus(USB) drive, compact disk (CD) or digital versatile disk (DVD), any typeof disk drive, or any other desired electronic storage medium.Instructions may also be stored in an attached (or internal) hard drive.The computing device 307 a may include one or more output devices, suchas a display (or an external television), and may include one or moreoutput device controllers, such as a video processor. There may also beone or more user input devices, such as a remote control, keyboard,mouse, touch screen, microphone, etc. The computing device 307 a mayalso include one or more network interfaces, such as input/outputcircuits (such as a network card) to communicate with an externalnetwork 306. The network interface may be a wired interface, wirelessinterface, or a combination of the two. Communications between thenetwork nodes are typically effected by exchanging discrete packets ofdata. Each packet typically comprises (1) header information associatedwith a particular protocol, and (2) payload information that follows theheader information and contains information that may be processedindependently of that particular protocol. In some protocols, the packetincludes (3) trailer information following the payload and indicatingthe end of the payload information. Server 307 a is further connected to(or includes) a mass storage device 307 b and 307 c, noting that themass storage device may be coupled to the server 307 a via acommunication link. The mass storage device contains a store ofnavigation data and map information, and can be a separate device fromthe server 307 a or can be incorporated into the server 307 a.

The device 310 a can be a system with its own memory resource(s) thatcomprises, for example, a volatile memory, such as a Random AccessMemory (RAM) and a non-volatile memory (e.g., digital memory, such as aflash memory). Connections to the device 310 a can be a wired orwireless connection to any other external device, such as a car stereounit for hands-free operation and/or for voice activated operation, forexample, for connection to an ear piece or head phones. The mobiledevice's connection may be used to establish a data connection betweenthe mobile device 310 a and the internet via a wireless macro network(e.g., 3G, 4G HSDPA, etc., and future generations thereof) or any othernetwork, for example, such as a Wi-Fi network or any future generationthereof, and/or to establish a connection to a server via the internetusing some other network, for example, such as an internet connection.For the example embodiment, an internet connection between the server307 a and the mobile device 310 a is established.

The server 307 a hosts a driver profile and a profit or risk profiledatabase 307 b and the interest or demographics database 307 c wherefactors affecting the profitability of several products are stored. Forexample, in one embodiment, the information stored for auto insurancemay be any or all of the following: total miles driven, percent of drivetime during night time, average speed (normalized for road speedlimits), the risk score of the areas where the driving occur (asnormalized by past incident databases in those areas), and estimatedloss ratio for future insurance at various rate levels. In anotherembodiment, the information stored for medical products may be any of:mileage during weekdays, number of routes to known malls, pharmacies ormedical buildings, and average speed. In another embodiment for wirelessservices: total time outdoors or number of routes to entertainmentareas, such as bars or malls containing movie theaters. In anotherembodiment, for liability insurance fleets of self-driven cars, the datastored can include total mileage of the entire fleet containing the samesoftware version and sensor configuration as well as the risk factors ofthe areas driven by the entire fleet as adjusted or normalized forvarious area specific risk factors. The Profile database 307 c maycontain the classifications that are derived from routes driven. Theclassification categories may be similar to those used by major adnetworks but new categories can also be created to reflect additionaldata from routes that are not yet captured in traditional demographicsclassifications used by ad networks. In one embodiment, such a newcategory can be, for example, “drives most of the time in theafternoon.” The vehicle portal server, referred to herein as server 307a, generally corresponds to one or more computing systems configured tocompute, store, and process data associated with one or more drivers orfleets of self-driven vehicles, as well as data associated withmerchants of interest to those riders and data provided by products andservice providers such as actual profitability of drivers that werepurchased their products or services. In one embodiment, such data canbe claims data for auto insurance or data consumption for wirelessservices. Such computed data can be stored in the driver and profit andrisk profile database 307 b. The server 307 a receives from any of aplurality of data providers 305 a-d, described below, supplementary datathat, when combined with geolocation dynamic data originating fromdrivers 310 a or fleets of vehicles 308 a, allows for the delivery ofpoints to drivers driving along specific route segments and theclassification of drivers in specific insurance risk pools. In theembodiment shown, the server 307 a may be accessible by any of aplurality of drivers' mobile devices 310 a (e.g., a mobile phone ortablet device) and the routes driven by their associated vehicles 310 bas well as by all the centralized controllers 308 a and their associatedfleets of vehicles 308 b. An application installed on a mobile device310 a may be required to communicate with the server 307 a and transmitdata such as encoded route information or receive messages and contentfrom a merchant's computing device 309. No application may be requiredon the fleet of vehicles 308 b as the data captured along the routesdriven can be communicated using an encoded format to the centralizedcontrollers 308 a of the fleet and then to clearinghouse 308 c thatconnects all the fleets from several self-driven car technology vendorsto the interactive system 300. Although only cars are shown in theembodiment illustrated in FIG. 3, it should be understood that othertypes of vehicles could be used as well, such as trucks, vans, orcommercial vehicles, according to the various aspects of the presentdisclosure. The server 307 a is also accessible via a plurality of othercomputing devices, such as computing device 310 c having a web browserinstalled thereon and which can be associated with the driver's mobiledevice 310 a. In the embodiment shown, a plurality of third-party dataservices is integrated with the information used by server 307 a toprovide the cash convertible points and ads that should be deliveredalong certain routes and to classify the drivers in profit/risk pools.In the embodiment shown, the data providers 305 a-d include: (1) A mapdata provider 305 a that includes, when available, speed limitsinformation. The map data provider 305 a delivers map services to theserver 307, with which various data overlay services can be providedincluding traffic data, turn-by-turn directions in case the driverdecides to visit one of the advertising partners, GIS data, or othertypes of information, such as speed limits on various roads and mileagebetween two points on the map along a specific road segment; (2) Ageocoded database showing frequency and severity of road accidents fromdata provider 305 b such as police reports and accident reports; (3) Anadvertising data provider 305 c capable of delivering ads that targetcertain driver profiles as determined by the server 307 a based on thehistorical routes driven by the drivers 308 a or capable of providingads of interest for the specific roads typically driven by the drivers308 a; and (4) A data provider owned by product and service companies305 d. In one embodiment, an insurance carrier data provider providingbids for commissions levels on specific groups of profit/risk classifieddrivers associated to their vehicles 310 b or a wireless service companyproviding bids (e.g., fees they would be willing to pay) to access thedatabase 307 b to market specific wireless data to specific profit/riskclassified driver groups, using an application residing on the mobiledevice 310 a. Data providers 305 d in addition to providing bids forcommissions or access fees also provide, over time, profitability datasuch as data utilization or claims data for the drivers that haveselected their products in order to improve the classification ofdrivers into separate profit/risk pools. In addition to product andservice data providers, intermediaries, brokers, agents may store indatabase 307 b additional information about the vehicle 310 b and thedriver using applications running on mobile device 310 a to presentforms or questionnaires at various points in time and as needed. Thisadditional information can be linked to the route information in orderto better classify the drivers and their associated vehicles inprofit/risk pools.

In some embodiments, merchants using specific ad networks can delivercash convertible points to the driver's accounts based on some routedriven. In some other embodiments, the advertising data provider 305 bdelivers advertisements to users who are drivers using an applicationrunning on mobile devices 310 a. In other embodiments, once the profileof the driver is associated with third party cookies left on a driver'scomputing device 310 c using, for example, the systems and methodsdescribed in FIG. 9, interest based advertising can be delivered on thecomputing device 310 c as the driver browses the internet on thatdevice. The advertising systems of the present disclosure can take manyforms. For example, in some cases, when a particular route is beingdriven, advertising corresponding to businesses located along that routecan be displayed to the user. In other embodiments, personalized contentis downloaded in advance in anticipation of the user driving alongspecific routes. In other embodiments, the delivery of the cashconvertible points is not related to the merchants along specific routesbut is related to the driver's profile determined by the server 307 aand stored in the database 307 b that is related to the market segmentthe driver belongs to as inferred by the routes typically driven by thedriver (e.g., example routes driven frequently on a Saturday night thatend in areas with several bars can infer a single/youth market segment).In all such cases, the advertising and cash convertible points providers305 c use server 307 a to publish targeted content on the mobile devicerunning a specific application or to access for a fee the driver profileresiding in database 307 b. Also, as illustrated in FIG. 3, the variousdata providers, drivers, and vehicle fleets may be communicativelyinterconnected with the server 307 a via a network 306, such as theInternet. Additionally, such a network may be used by users of mobilecomputing device 310 c or merchant computing devices 309 forcommunicative interconnection to the server 307 a.

FIG. 4A is a schematic diagram illustrating minimizing battery andbandwidth utilization and minimizing the amount of data transmittedwirelessly by the driver on costly outdoor wireless networks. In manyembodiments, GPS may not be set during specific road segments, and alocation feature of the driver's mobile device may be turned off. Eitherbecause the driver chose not to use GPS while driving or because thesystem has determined that it has stored sufficient prior routes in thearea being driven that it can determine a route path with a high degreeof confidence despite using a much lower accuracy location method (butless battery draining and more bandwidth efficient). In this exampleimplementation, cell tower ID and network-based triangulation based onpower received by the device and time advance measurements from severalcell sectors being monitored by the device during the normal course ofoperation on the cellular network can be used to map approximatelocations that can be snapped-to most probable roads on a map usingmatching that takes into consideration, for example, the importance ofthe roads, the probable destination of the driver based on pastbehavior, and the probable route used by the driver based on previousroute observations. Once location data points have been snapped tolocation points on a road segment, those location data points cansubsequently be used first, to determine mileage and distance, andsecond, to approximate average speed as instantaneous speed may not beavailable considering error probabilities of the location information.For example, in FIG. 4A, a user does not permit high-accuracy GPSlocation methods, and the system must rely on network-basedtechnologies, such as cell ID or cell ID with time advance, to determinea route with high confidence. A vehicle has its location captured bycell towers or sectors A 403, then B 404, then C 405, then D 406, andthen E 407. Because the only roads going through the coverage 410 ofcell A 403 and the coverage 411 of cell B 404 are road 1 415 and road 2414, then the driver is assumed to be on one of those two roads and thepoint of origin would be in the segment of those roads included incoverage 410 of cell A 403. The system assumes an approximate point oforigin being snapped to the roads at either point R1-O 401 or R2-O 409.If the system has stored a usual route along road 1 415 going from R1-O401 to R1-D 402 from prior measurements based on high accuracy GPS, thenthe system calculates a high confidence factor that the vehicle istravelling along road 1 415 through the points R1-O 401, then G 416, andthen H 418, as opposed to road 2 414 through the points R2-O 409, then F417, and R2-D 408. As the vehicle is captured by the coverage 416 ofcell D 406, the system calculates an even higher confidence factor thatthe vehicle is on road 1 415, because road 2 414 is not within the areaof coverage 406 of cell D 406, and determines that the driver is along ausual road previously stored road 1 415 with a previous destinationpoint on point R1-D 402, which can be confirmed when the vehicle isregistered on cell tower E 407. If the driver had no prior usual roadsstored, then the system can approximate distance and speed based on aroad segment travelled along the points A 403, B 404, C 405, D 406, andE 407 based on approximate known coverage of those towers and existingroads within the coverage to determine the most likely road taken thathas the highest road traffic and snap the path ABCDE to a route alongroads covered by those cell towers or sectors.

FIG. 4B is a schematic diagram illustrating an example embodiment thatreduces battery usage and data collection and transmission activity.Such reduction can be managed by a software compression and dataoptimization module 460 residing on a mobile device. The module monitorsseveral device characteristics, such as battery level, power level andsignal quality of surrounding networks (e.g., Wi-Fi or cellularnetwork), phone status as determined by the application stationary or inmovement in a vehicle, time of day, and connection to a power source.One of the module's main functions is to regulate the frequency of theGPS measurements and amount of data received and transmitted back to acentral server of the system.

Significant reduction in data transmitted is possible with an exampleobjective of predicting demographics and profit/risk potential forvarious products and services as measurements of, for example, location,speed, and mileage need only be approximate. In one embodiment, thefrequency of GPS measurements and frequency of data transmission (realtime versus delayed) is determined by a centralized server based on thetype of product and service for which profitability is being predicted468. The data compression and data optimization module 460 can executecomputer codes to regulate the amount of data collected and transmittedon outdoors wireless networks and the amount of device CPU utilizationbased on a level of precision required to estimate routes driven,mileage, and speed as pre-determined by an accuracy thresholds settingset by the system and sent over wireless networks from time to time.Those parameter settings 468 can be stored in the device memory anddatabase 461. In one embodiment, the code on the device executes a firststep for optimization to determine if the vehicle is moving 463, if yes,the device compressions and data optimization module causes the deviceto detect if it is connected to the vehicle 464 or to determine if thelast known location is approaching the boundaries of predicted areas ofhigh frequency location 456. Those areas are determined by the centralserver of the system and are around each road junction where the driverneeds to make a decision as to which road to take. The increase inaccuracy in location determination within those specific areas isnecessary to be able to significantly reduce measurements and datatransmission as soon as the driver exits those areas 455. Thecoordinates of those areas of high accuracy as well as the coordinatesof the roads surrounding the last known parking location 469 aredownloaded in preference on Wi-Fi and while the driver is located athome or work or any other long term origin or destination point. Thisreduces data transmission and bandwidth utilization on outdoors networksand reduces battery consumption while the device is located in an areaclose to antennas and transmitters so that the device does not have touse strong power levels to transmit or download data over longerdistances. Once a driver exits those areas, as per FIG. 4B while drivingon segment Junction AA 455 to Junction BB 456 which is the entry pointof the next area, area B 453, of high frequency measurements, variousparameters (e.g., path, speed, and mileage) that are required forprofitability prediction algorithms can be determined with very lowfrequency location measurements since the road segment from Junction AAto Junction BB is the only road segment between the two points and thepath and distance on this road segment is provided by map providers thatmay be accessed by the system. Since most roads have already beengeocoded by map providers, the path and distance between two roadintersections is known, so the distance and average speed between twointersections can be determined without many intermediate measurements.In some embodiments however, the areas of increased locationmeasurements as determined by the central server can be increased ordecreased, or additional areas along the path between two roadintersection points can be inserted depending on the classification ofthe driver. In one example embodiment, the central server softwareconfiguration may cause the areas of high frequency high accuracymonitoring to change, or new ones to be inserted if the driver isclassified as a high risk from prior route analysis for insurancepurposes. In such example embodiment, intermediary areas of higherfrequency and higher accuracy measurements may be required to monitorsudden acceleration and sudden burst of speed between intersectionpoints, such as while driving along segment Junction AA 455 to JunctionBB 456. In another example embodiment, the prior classification can bebased on too-limited set of route information, perhaps because thedriver is a new user of the device-based application and the ability topredict habitual routes or driving patterns has not yet beenestablished. The ability to modify how and when the driver's drivingpatterns are being tracked and monitored and to increase or decrease thefrequency and/or the accuracy of location measurements based on a priorclassification of the driver related to the predicted profitability forcertain products and services, increases the accuracy of suchclassification while minimizing as much as possible battery usage anddata transmission. If the classification is validated over time as beinghigh risk, the centralized system can send targeted messages to themobile device of the driver encouraging changes in driving behavior thatmay result in a re-classification of the risk and predictedprofitability for various products and services.

Furthermore, additional compression of data can be used in order toavoid costly data transmission of promotional content expected to bepublished or listened to while on the road. Content can be audible orvisual displayed messages from merchants and service providers along aroute that matches prior visits or matches potential interest of thedriver or matches an advertising potential as set by a centralizedprocessor responsible for selecting merchants (e.g., popular franchises,popular department stores, or points of interest). The devicecompression and data optimization module 460 can use algorithms to causetransmitters 462 to attempt to send a small data set to the centralizedsystem server and to do so in bulk and in a compressed format. Someembodiments achieve this objective by taking advantage of known usual orhabitual routes that a user will likely take in the future andrequesting and receiving content for those routes while the vehicle hasstopped, and the driver has been connected to one of its usual Wi-Fihotspots, such as a home, work, or coffee shop hotspot. For example, inFIG. 4B, if Junction point A 450 to Junction B 451 is a usual route,content related to that segment may be downloaded prior to the driverbeing on the road, while at home, while no content would be downloadedin advance if it is related to route Junction Point A to Junction C 454as this route (A to C) is rarely taken. Much of the data captured onprior route and much of the data that is expected to be used during thenext expected route (as predicted based on prior historical routeanalysis and time of day) can be stored in device memory and database461 and captured data may be transmitted preferably back to the centralsystem server while the device is stationary.

In FIG. 4B, it is shown that the general macro area in which the deviceis generally located can be divided into areas (such as area A 457 andArea B 458, and when the device is, for example, located in area A 457,the coordinates 469 of the roads in area A can be sent to the device atthe appropriate time before entering that area and the coordinates ofthe roads travelled while in area A can be sent to the server at anappropriate time while in areas of high data transmission, such as areas452 or 453, or while exiting the area. To further reduce data packets,the local road map may be in the form of geocoded information compressedat an appropriate resolution of location accuracy and sampling frequencyto obtain an approximate estimate of distance, speed, and directionbased on a required parameters set 468.

While driving along usual routes at usual times of the day, the systemmay assume that the commute is fixed and the data collection module 460can cause the capture of a sample of a limited set of data and encode itappropriately, for example, a small sample of data collected at thejunction points and various cell ID measurements instead of GPSmeasurements may be enough to verify and reconstruct with a high degreeof confidence the entire route. Speed can also be reduced in minimumdata sets. Route segments travelled at approximately constant speed maynot transmit all the instantaneous speed along the road, but a singleaverage speed along a beginning point and an end point provided that astandard deviation from this average speed is relatively small. In manycases, when the driver has been repeatedly classified as low risk forcertain insurance products or high profitability for certain dataproducts, average and actual speeds do not need to be constantlycompared to speed limits on the road segment travelled, and if it iswithin speed limits for the roads travelled during the measured samples,the respect of speed limits on entire road segments may be assumed as acontinuous real-time measurement of speed would have a negligible impacton inferred profitability of the driver for various products andservices. There may also be other metrics associated with driving alongthe a route that is captured in encoded form in addition to speed androute, such as, for example, acceleration, deceleration, time ofday/week/month, and cumulative averages such as cumulative mileage. Suchcollected data can be compressed by the data compression and dataoptimization module 460 and may be stored in any appropriate format onthe device, such as a dynamic cookie or other persistent identifier thatcan be linked to the advertising ID of the device, such as, for example,an Android advertising ID. This information can be stored in the devicestorage and database 461 in a SQL lite structured database, for example.The data compressions and data optimization module 460 uploads theencoded data to the centralized system server when an efficient networkconnection is available, a threshold of data collection is reached, athreshold of local memory utilization has been reached, or a preset timeinterval has been reached.

FIG. 5 is a flow diagram illustrating collecting driving information andclassifying drivers, according to an example embodiment of theinvention. The example method 500 includes collecting 505 drivinginformation using a device associated with a driver and a vehicle. Thedriving information includes routes driven by the driver in the vehicle,and can also include mileage, time of day, and speed. The collection ofthe driving information can be enabled and disabled based on locationand movement of the device. The method 500 further includes encoding 510the driving information and transmitting the encoded driving informationto a server, determining 515, and storing in a database associated withthe server, predicted future typical route segments that the driver islikely to travel over a certain period of time and associated times ofday based on the encoded driving information, and classifying 520 thedriver into one or more groups based on the encoded driving information.

FIG. 6A is a flow diagram illustrating presenting an advertisement orpromotional content to a driver based on collected driving information,according to an example embodiment of the invention. The example method600 includes associating 605 an advertisement or promotional contentwith at least one of the typical route segments (e.g., drivinginformation collected by the above example method 500) driven by thedriver in the vehicle. A point is determined 610 along the associatedroute segment at which the advertisement or promotional content is to bepresented to the driver to provide a sufficient amount of time for thedriver to take an action associated with the advertisement orpromotional content, and the advertisement or promotional content ispresented 615 to the driver via the device when the driver drives alongthe associated route segment in the vehicle and reaches the point.

FIG. 6B is a flow diagram illustrating determining insurance risk for adriver or fleet of fully or partially self-driven, according to anexample embodiment of the invention. The example method 650 includesclassifying 655, into one or more groups, a group of drivers or a fleetof self-driven or partially self-driven vehicles, and determining 660 aninsurance risk for each driver or fleet of self-driven or partiallyself-driven vehicles based on driving information of the driver or fleetof self-driven or partially self-driven vehicles (e.g., drivinginformation collected by the above example method 500). The method 650further includes presenting 665 the insurance risk classification to aplurality of entities, receiving 670 requests from at least one of theentities to provide insurance for or offer services to the driver orfleet of self-driven vehicles, and selecting 675 one of the entities toprovide insurance for or offer services to the driver or fleet ofself-driven vehicles.

FIGS. 7 and 8 are schematic diagrams illustrating tracking of routesdriven by a driver in a vehicle, according to an example embodiment. Itcan be expected that there will be a level of resistance from consumersto be tracked. Tracking a consumer's driving habits, however, isimportant to accurately determine routes typically driven and relatedparameters, such as time on the road, mileage, typical destinationpoints at various time of the day, speed, area driven through, etc., inorder to predict the profitability of a driver for various products andservices and to keep the driver engaged with the application if some ofthe product and service providers are requesting access to the mostprofitable segments for promotional activities.

Prior methods that take into consideration mileage or driving behaviorfail because they do not deliver permanent tracking. For example, somediscounts on car insurance products have been offered as an incentivefor low mileage or good driving behavior during a period of time inwhich tracking occurs (either through an external GPS device or othermethods), but this approach presents very significant issues: once thediscount is obtained there is very little incentive to continue to shareprivate information and accept being tracked over longer periods oftime, which can be important to properly assess the economics of theunderlying risk. Service providers may face fraud on the part of theusers as there is very little incentive for the users to keep theexternal GPS device tuned on during the initial period when they aretravelling for long trips during the tracking phase or, alternatively,they end up giving discounts only to the low mileage customers withoutbeing able to raise rates for the others, thus making their ratingmethod economically unsustainable. Privacy concerns can be overcome ifthe consumer receives something in return for relinquishing privateinformation (as demonstrated by social networks). To secureauthorization to track the drivers over long periods of time, thedisclosed embodiments address ways to overcome resistance of the driverin terms of privacy by providing a benefit to the driver. In oneembodiment, points that are convertible to cash, incentives, or merchantcoupons are provided to the driver that downloads a specific applicationand activates it on his mobile device with all or a portion of theadvertising revenue going to the driver. So, contrary to the traditionaladvertising model, where advertising revenue goes to the applicationdeveloper, the methods and systems disclosed herein can direct thisincome stream to the user and optimize consumer acceptance of privacyencroachment and provides a more permanent offsetting reward system tothe consumer by funneling all or a portion of the advertising revenueback to the consumer.

Another problem with prior methods is that the delivery of advertisingon mobile devices in a moving vehicle does not work well. The device islikely to have its screen turned off with the driver concentrated on theroad, or the device is showing a turn-by-turn navigation application orplaying music. Any ad delivery mechanism must either integrate itself inthose applications or be itself audio based with minimal or no datainput from the driver (i.e., no possibility of click based advertisingor display advertising). Another problem with prior methods is that anyadvertising that is specifically related to nearby merchants andcommerce has very limited value as it is not delivered with the propertiming to impact an actionable result (e.g., re-routing a driver fromhis usual route while on the road). Furthermore, there are no efficientequivalents to the “click to action” or “converted clicks” in thephysical world. Online, one can measure if there is a follow up actionafter clicking on an ad, such as a purchase of the product or a formfilled, and can reward the publisher of the advertisement based onconversions. In the physical world, the follow up action is, forexample, a visit to a store. Thus, a clear link between a promotionaltrigger in the physical world and a physical action (a visit to a store)does not exist. To address these obstacles, an equivalent of a convertedclick in the physical world can be created to predict the profitabilityof drivers for products and services. The value of cash incentives orpromotional incentives can be optimized by serving targeted ads on thedriver's mobile devices or desktop browser that take into considerationthe routes being driven, when those routes are driven, the merchantsalong those routes, and the driver's inferred profile, and by providingall or a portion of the advertising revenue back to the driver. In oneembodiment, an interactive voice advertisement is stored in advance(while the device is stationary with a Wi-Fi connection, for example) onthe device's local storage based on expected usual routes, and theadvertisement is played when the device estimates that the driver isapproaching an exit or road intersection that would lead to themerchant, placing the interactive pre-recorded voice advertisement atthat moment. The on-device application listens for an audible responseof the driver and, through voice recognition, determines if the driverhas repeated some key words (such as a brand name or a number)determined by the advertiser/merchant and requested in the advertisementrecording. If it is confirmed that the driver has in fact repeated thosewords through voice recognition software on the device, a certain numberof cash convertible points can be deposited into the driver's account.An additional and likely larger number of points can also be depositedas set by the merchant or advertiser connected to the ad network if,within a specific time (e.g., number of days or hours) the driveractually visits the related establishment.

FIG. 7 illustrates an example embodiment that uses prior knowledge ofusual routes driven as well as tracking while in a vehicle to delivercash convertible points to the driver. The embodiment changes the boundsof a high frequency high accuracy region around road intersections totake into account that a voice ad needs to be played much sooner beforereaching a key intersection given the speed of the vehicle. The driver706 is known, through historical analysis of the whereabouts of thedriver's mobile device 711, to frequently drive around a specific timeof the day from the point of origin O 700 to a destination point D 705.Because the driver has a history of going through the intersection pointC 707 around typical times of the day when departing from point O 700,the system can downloaded, while stationary at the driver's residence orplace of work, the coordinates of the geo-fenced area A1 701 to limitthe increase of frequency and accuracy of location measurement to thisspecific area of the road segment according to the methods disclosedherein, and reduce battery and bandwidth consumption. If anadvertisement is to be delivered, the centralized server 819 can changethe area A1 into a new computed area A2 if an advertisement is to bedelivered before Point C 707. This is because an advertisement messageneeds to reach the driver much sooner, and the high accuracy locationneeds to be triggered much sooner when advertisements that encourage thedriver to take a detour at point C 707 to reach, through a detour routeAA 703, a place of commerce located at point D2 706. The shape of areaA2 can be calculated by the server 819 and rely on prior historicalspeed of the particular driver on road O-D around that segment. Thespeed is important in determining how much sooner before arriving atjunction point C 707 the driver would need to receive information aboutthe detour. Information is in one embodiment delivered throughinteractive audio ads and can contain, for example, time it would taketo go through the detour, number of cash convertible points received ifthe driver takes the detour, and which merchant is advertising, as wellas a short more tailored message 712 from the merchant. Once the shapeof area A2 is determined based on prior speed and routes, it can bedownloaded in advance in the device memory 715 along with the audiomessage recorded by the merchant 830 while using a first party adnetwork 805 to target specific drivers that typically drive throughspecific intersection points, such as point C 707, that can be convincedof a minimal detour time to the merchant's place of business D2 706.

The device memory and storage 715 can be used to optimize bandwidthutilization and battery usage. An interactive merchant advertisement712, the coordinates of nearby road segments, and the computedcoordinates of the new area A2 can be stored in advance (while on Wi-Fiat home or work, for example) in the device memory and storage 715. Thedevice processor 716 causes the proximity module to compare actuallocation measured by the location service 714 to those road coordinatesand area A2 coordinates to detect proximity to areas of high accuracy atintersections, ad trigger zones (such as A2 and the actual route),mileage, and speed of the vehicle. The local application stored in thememory of the mobile device executes commands that play the interactiveaudio as it approaches the point where a detour may be needed. Usingvoice recognition technologies embedded in the device, the applicationrecognizes if the user responds to the audio ad on the device speakers713 with predetermined interactive responses (such as, for example,Yes/No/“I Love Walgreens”). The answers are captured by the microphone713 and analyzed through the device voice recognition, and the volume ofthe response is captured by the application (e.g., as a measurement ofenthusiasm) and entered as a parameter in a behavior profile module 809.

The interactive merchant advertisement 712 can take the form of filesthat take much storage and time to download. Because area A2 is alsostored on the device, it enables the storage of the voice files to bestored as well. Large files are required if the merchant chooses to havea long interaction with the driver. The advent of artificialintelligence enables maintaining long conversations. In one embodiment,the merchant can use the interactive voice ad to fill in forms, givingout cash convertible points for each answer. So instead of being a onequestion 712 and one voice answer 713 from the driver, the interactivead could be a questionnaire with multiple questions and multiple answersand, thus, the voice files could increase in size very quickly,requiring Wi-Fi/local hotspot side download to save bandwidth on costlywireless networks.

Short interactive ads are as illustrated: advising of a detour, detourlength, points if ad is acknowledged (with voice recognition), andpoints expected if the driver visits the merchant's place of business. Alonger interactive ad could involve, for example, trivia games about thebrand or an acknowledgement of key attributes of the product (“do youlike music,” “do you like sounds that can shake walls,” “next exit turnleft to go to Johny's Ultimate Loud Speakers”). Other long interactivequestionnaires could also be sent from time-to-time by the system (asopposed to by the merchants) to capture driver provided information 825shown in FIG. 8 that can contribute to the accuracy of the profitabilitypredictions for various products and services. For example, for autoinsurance profitability predictions, some of the information thataugments the route data could be captured in the same way throughinteractive voice questionnaires using the same methods disclosed here.Complementary information that can be captured from a driver (forexample, car VIN number, driver's license number, gender, birthday,make/model/year of car, current mileage on car, name, address, emailaddress, current insurance carrier, current rates, social securitynumber, various authorizations such as for insurance products), allowingthe intermediary using the system to become the agent of record on thecurrent insurance policy and to switch insurance carriers if need be,assuming similar rates and coverage.

The reward module 805 determines the portion of the points that accrueto the driver. This can be based on the amount of data that has beenprovided by the system-generated questionnaires 825. In one embodiment,if all system-generated questionnaires are answered, 100% of any adrevenue may accrue to the driver. In one embodiment, the reward module805, determines if the driver has visited the merchant establishmentwithin a predetermined number of days based on measurements of thedevice's location services module 714 and the proximity module 717 or ifthe captured SSID addresses of the nearby Wi-Fi hotspots matches themerchant SSID. A typical implementation of this requirement can requirethe merchant to surround his place of business with a geofenced area 704on the console of the First Party Ad Network 830. The geofencedcoordinates can be sent to the device's memory and storage 715 andcompared using the Proximity Module 717 to determine if the driver hasvisited the merchant's location and stayed there for a specified amountof time. This approach is battery and bandwidth intensive.

To detect presence at the merchant establishment, in one embodiment,unless a user turns Wi-Fi off on his mobile device before he leaves hishouse, the device automatically scans for available Wi-Fi networksnearby. Smartphones and other devices typically use both passive andactive discovery—they passively listen for Wi-Fi access pointsbroadcasting to let nearby devices know they are available, and theyactively broadcast requests searching for nearby access points. Due tothe way Wi-Fi is designed, a device searching for Wi-Fi access pointsincludes its MAC address as part of the “probe requests” it broadcaststo nearby Wi-Fi access points. This is part of the Wi-Fi specification.To verify that a user is in a store, one can verify his/her MAC addressas well. Some mobile device brands mask/randomize the MAC address.

In another example embodiment, to detect the presence of the driver atthe merchant's establishment, bandwidth and battery usage are reduced byhaving the merchant enter the Wi-Fi SSID of the hotspot in hisestablishment with the advertisement(s) downloaded. For example, theapplication can sense whether the device is within range of a merchantWi-Fi hotspot. The merchant/advertiser can be linked to the devicethrough the application running on the device, and the merchant canstore its own merchant SSID Wi-Fi on the driver's device whiledownloading an advertisement. If a visit occurs as a result of theadvertisement, the mobile device application can detect the merchantSSID at the merchant's establishment and recognize that a visitoccurred. Once the visit is detected, the application can trigger adeposit of cash-refundable points in the driver's account with thenumber of points/amounts deposited determined by the merchant when heplaces the advertisement. The number of points credited to the drivercan depend on amount of time spent at the merchant's establishmentand/or frequency of the visits. In one embodiment, the Wi-Fi accesspoint may cover only the cash register location. Using the merchantWi-Fi information, the location service module 714 does not have to makeprecise location determinations against a geofenced area 704. Instead,as the device enters area 704 calculated by the central server, it canstart verifying from the SSID broadcast which ones correspond to theSSIDs entered in the console 830 to verify the visit of the driver tothe merchant location, thus completing the delivery of cash convertiblepoints to the driver through the reward module 805. If the visit happenswithin the period advertised, a number of points can be deposited intothe driver account. This method of correlating visits to drivers is verycompelling for advertisers as it allows them to provide discounts (or inthis example, cash back) to a very select segment of the populationwithout having to train their staff and without giving equivalent margineroding discounts to all walk-in customers, thus reserving priceintegrity of their merchandise. The target can be those individuals whopass by their place of business frequently and are within apredetermined number of detour minutes, for example, of their locationsat specific periods of time. For example, a merchant may prefer to catchdrivers on their way back from work rather than in the morning forcertain establishments, or on their way to their typical grocery store.Thus, the disclosed embodiments create a form of real-world “click toaction,” which is when an advertiser pays a publisher not only todisplaying an ad, and not only for a click on the ad, but also in thecase something is actually purchased while on the advertiser web site.In the physical world it is extremely difficult to create a “click toaction” equivalent; however, the disclosed methods and systems allowsmerchants to detect the presence in their place of business of theprecise visitor they sent an ad x days or x hours ago. Because the causeis so direct and measurable, merchants will be likely to pay for suchsuccess based advertising a much higher amount than traditional ads,especially because the money goes directly to consumers and is viewed asa product discount for the consumer rather than an advertising cost forthe merchant. The money is paid to the driver and not to an applicationdeveloper or content provider and, as such, can be a key incentive forthe driver to leave his location services on at all times and allow theon-device route determination application to request locationinformation at any moment.

The targeting ability of promotional messages based on prior routesrequires knowledge of historical routes, and the ability to redefine theareas of high frequency high accuracy location measurements. It requiresthat ad networks have access to route information databases 807 andprofile databases 806 to deliver promotional messages to participatingdrivers, and to detect if a specific driver takes a detour to a merchantestablishment. The tailoring of the first party ad network 805 is uniquebecause it targets drivers based on prior routes and expected futureroutes, not on current location, and reshapes area A1 to area A2 basedon speed from prior routes and other road factors, such as speed limitson those road segments. Ad networks offer location based advertising,based on coarse prior location knowledge (city, zip code, and real timecurrent location), but the disclosed methods and systems provide routesegments frequently driven as a segmentation approach to advertiser'scampaign managers and a system that can effectively re-route at specificintersection points that are used as triggers for advertising in a waythat reduces significantly data and battery usage through the reshapingof location intensive zone in anticipation of a detour decision.Furthermore, the ability to link the merchant SSID to the advertisementand the driver enables a low resource method to detect visits in thephysical world as a direct result of placed advertisements.

FIG. 9 is a schematic diagram illustrating real world cookie-likefunctionality. Online, advertisers typically use cookies to collectanonymous information about a website's visitors. The advertisers thenuse this information to create profiles containing specific detailsabout a user and to display relevant ads based on the sites visited bythat user and the user's activities on those sites. The profiles areclassified according to certain categories of users, such as userslikely interested in mortgages or users likely interested in travel,etc., or, more simply, what the visitor searched for on an online site,such as “smartphones unlimited data.” Typically, websites store cookiesby automatically storing a text file containing encrypted data on auser's machine or browser the moment he or she lands on a page online.Whether it is a permanent or a temporary cookie, the idea is to create a“log” of the user to facilitate future visits to the site. When thesecookies are collected to create a certain idea about a user, that iscalled cookie profiling, or web profiling. Collated data may includebrowsing habits, demographic data, and statistical information, whichare what marketers are after in order to mark a user. Cookie profilingis performed by advertisers networks, but the cookies they need tocreate these profiles are obtained from several online sources, mostlyfrom administrators of popular sites receiving millions of trafficmonthly, and third party cookies are used by ad networks to identifyconsumers across different mobile web sites. When a consumer visits aweb site, the pages they visit, the amount of time they view each page,the links they click on, the searches they make, and the things thatthey interact with, allow sites to collect that data, and other factors,to create a “profile” that links to that visitor's web browser. As aresult, website publishers can use this data to create defined audiencesegments based on visitors that have similar profiles.

An example embodiment of the disclosed methods and systems createssimilar, cookie-like functionality for advertisers to provideinformation and profiles based on actual physical places visited andactual routes driven instead of websites visited and browsing activity.The methods and systems disclosed herein can create a demographicprofile and targeting categories that are linked to routes driven andplaces visited, but integrate seamlessly with what existing ad networkshave already built for the online world.

FIG. 9 shows methods and systems to determine a driver profile in termsof demographics, interests, and behavior as inferred from the driver'susual routes driven. FIG. 9 shows the apparatus and the steps taken by acomputer program giving instructions to the CPU of the central systemserver 819. Storage 938 contains data structured in a databaseaccessible by the CPU of centralized system server 819. The database 938contains demographics data for geocoded areas, such as types ofestablishments in an area (high end retail stores, typical suburbanservice and restaurant chains, offices, manufacturing, etc.), averageage, income, ethnicity, residential versus commercial zoning percentage,single home versus apartment percentage, and average house prices forspecific geocoded areas.

Each route taken follows a path that may cross several areas withdifferent demographics. For example, path 1 903 crosses area A 906 andarea C 908, whereas path 2 904 crosses area B 907 and area C 908. Thepath of each route and the area types travelled are stored in the routedatabase 939 and compared to the demographics database 938 as well asall the parameters, attributes, and factors of the route 940. Routefactors in one embodiment are: demographics of point of origin O 901,demographics of destination point D 902, demographics of areas crossedby the path driven between O and D, distance driven between those twopoints, number, type, and location of intermediary stops 905, time ofday, frequency of route between 0 and D, speed on road segments betweenO and D, and average route speed, are provided to module 940 forprocessing and inference of a profile.

At step 920, each specific route is associated with several demographicsand interest categories based on the route factors with each assigneddemographics/interest category having a calculated confidence factor asset by an algorithm and its underlying business rules and assumptions.An example of assigning demographics and interests based on route isillustrated in FIG. 9, which provides an example of two routes taken bythe same mobile device/driver from the same point of origin 901 and tothe same destination point 902. Point O of origin 901 is located in Area1 906, which is, according to database 938, located in very high incomeresidential area, and destination point D 902 for the purpose of thisexample is located, according to the database 938, in area C 908downtown in financial districts with a high concentration of officebuildings and high end bars. A driver from Point O to Point D can,therefore, be assumed to be a high income professional commuting to workwith a relatively high confidence factor. However, if the same mobiledevice/driver is seen also to drive along path 2 to go from point O todestination point D, the profile attributed to the driver could becompletely different because path 2 goes through area B, which is,according to database 938, highly industrialized, with a highconcentration of storage areas and manufactures, and there are a largenumber of stop points in between. Furthermore the path 2 mileage is muchhigher than path 1, which is the shortest route between O-D. The moduleat step 921, given those route factors 940 can then attribute a highconfidence factor to a different profile to the driver: a working classindividual picking up and delivering merchandise along a known route anda much lower confidence factor to a profile of a high incomeprofessional commuting to work despite the route's similar demographicsat the point of origin. Step 922 makes the final decision as to whichprofile to attribute to the driver given a multitude of routes by thesame driver and a comparison of the confidence factors for each route.If, for example, path 1 903 was an exception and the vast majority ofthe routes were on Path 2, the profile attribute to path 2 is determinedto be the final profile of the driver (unless of course beingcontradicted by future routes over time, at which point the profilechanges to accommodate the new confidence factors weighted with thefrequency of the new routes). Similarly, the time of day can also beused to determine various confidence factors: if arrival time atdestination point D using path 2 is late mornings, it is unlikely thatthe driver is an office worker, even if he may be going through path 1903 very often but late at night, as this may indicate a young deliveryworker going from time to time to the bars downtown using the shortestroute. Once the final profile is determined, it is stored in the profiledatabase 924 where it is used in step 925 for further use by first partyand third party ad servers and ad exchange platforms.

The profiles created by the disclosed methods and systems are “native”to the physical world. They are based on an analysis of routes and pathstaken by the driver and are defined through movements, mileage, time ofday, and impact the classification for profitability of products orservices. In one example embodiment, native route-based categories ofprofiles can be called “high mileage,” “outdoorsy,” “commute time mostlyon highways,” “weekend trip fan,” “goes weekly to low income stripmalls”). Route-based native profiles are different from profiles thatcan be obtained from the knowledge of online browsing activity. If,however, the profiles are merged or synchronized with online browsinginformation, route-based native profiles can be both confirmed and alsonuanced, and the classification of the driver can be made more accurate(e.g., in the example above, even though the person above could appearas a young working person with modest income, an online view of webbrowsing activity may indicate heavy studying in the medical field, thusmaking that individual's long term earning potential very high.)

Typically, online information can allow advertisers to target consumersbased on demographic characteristics captured and supplied by thirdparty ad networks, such as, for example, education or age, behavior(based on actions related to interest, keywords, purchase intent, andlife stage), location, mobile device business category, and context(what is going on around the consumer at the moment when the ad is beingserved), purchase history, interests, and lifestyle. Advertisers cannarrow the focus of their ads to reach, for example, only moms, luxurycar drivers, Latinos, or business travelers at specific locations ortimes of the day. There are several issues, however, when the user usesseveral browsers, account IDs, and several computers and devices toaccess information. It becomes very difficult to derive a unified visionof the browsing habits of the user. Methods exist to develop a devicegraph to link all devices to the same individual. Methods also exist(using probabilistic approaches to link IP addresses to the same user)in order to address cross-browser usage, however those methods are notprecise and are delayed. The methods and systems disclosed herein reducedelays and inaccuracies and augment a user's profile across severalbrowsers with route-based native profiles. There is a significantbenefit in integrating the profile derived from the physical world(usual driving routes, miles travelled, home and work locations) withthe online advertising platforms, profiles, and cookies throughsynchronization as disclosed herein. Augmenting the profile of onlinethird party advertising networks 819 allows for a better understandingof the demographics and interests of an individual.

In addition with real-time integration of the online/physical worldprofile, better results are achieved. A real-time integration allows are-categorization of the profile that is more precise and faster thanwhat an ad network would provide alone. As an example of the real-timebenefits of the remapping capability, one could confirm in many casesthe immediacy of certain life events more accurately and faster usingroute information when used in combination with web browsinginformation. For example, a sudden onset of web browsing for a house ora car is typically indicative that a major purchase may happen soon,however it is only when routes around residential areas during theweekend with multiple stops are detected or when multiple routes to cardealership are detected that there is a confirmation that the purchaseis likely immediate and, as a result, certain products and services,such as home insurance or car insurance can be marketed with increasedintensity.

Cookie syncing works when two different advertising systems (platforms)map each other's unique IDs that they have both gathered about the sameuser. Steps 925 et. seq. prepare the data in database 924 to beintegrated with a partner third party advertising network. A priorpartnership should be established so that data can be exchanged alongpredefined interfaces back and forth through the data interfacesexchange module 952. Step 925 maps route-based, “native” profiles in thephysical world to online profiles typically used by online ad servers bystripping the route base information and relying instead on the deriveddemographics/interests set at step 922. For each driver, the profilefrom physical route is, therefore, associated with an estimatedequivalent demographics/interest classification used by the partnerthird party online ad network.

Often, the partner third party ad network 819 may have a device graphfrom prior analytics and, therefore, may have a device ID associatedwith its profile or cookie ID. In addition, through the first partyapplication 933, which in one embodiment is a website where drivers canconvert their points into cash and residing either on the mobile deviceof the driver 931 or on a computing device running such application 932,a cookie synchronization process 934 can synchronize the identities ofthe driver with cookie ID's information already collected by the thirdparty ad network 819. Example steps taken by the synchronization modulecan be as follows:

-   -   Driver visits first party application site 933 that contains an        ad.    -   The browser sends an ad request to a third party ad network 819.    -   The ad network sends back the request and creates a        (third-party) cookie.    -   The ad network 819 redirects (http redirect) the ad request to        the pixel URL on the marketing data provider side 930, passing        the user ID in the URL parameter. The marketing data provider        930 reads its own first party cookie, or creates a new cookie,        and saves the user ID passed from the ad network along with its        own user ID in a “cookie-matching table.”    -   To make the sync bidirectional, the marketing data provider        redirects back to the ad network, passing its own ID in the URL        parameter. The ad network receives this request, reads its own        cookie, and stores the marketing data provider ID along with its        own ID in the cookie-matching table.    -   Now, both the ad network and marketing data provider have each        other's user IDs in each other's databases.

There are different types of identifying devices ID in android devicesfor example: Unique number (IMEI, MEID, ESN, IMSI), MAC Address, SerialNumber, ANDROID ID, and ad ID, etc. that can be synched in variousembodiments using both probabilistic methods with IP addresses or logininformation across major web sites.

At step 926, with the partner 3rd party ad network and the inferencemodule 940 both having synched the device ID and cookie IDs the systemproceeds to augment and synchronize each other's understanding of theinterests, demographics, and profiles of the same driver/web-user.

Step 947 compares the profile from step 925 derived independently fromthe routes with the profile associated with the device/ID/cookie ID atthe third party ad network and derived from web browsing activity. Thereal-time matching module 947 that maps the targeting categories derivedfrom routes to the targeting categories traditionally used by thepartner ad network 819 is achieved using the confidence factorscalculated during steps 920 and 921 and a set of business rules providedby the partner ad network. As the confidence factors change so does theprofile, making the matching process a real-time process that matches inreal-time any changes in route behavior in the physical world that mighthave gone un-noticed online. If the profile characteristics are the samewithin a threshold confidence factor, then the inference module uses thedata exchange module 952 to send to the third party ad network theroute-based profiling parameters through steps 951, which are nowconsidered consistent with the driver's online persona, interest, anddemographics. As an illustrative example, one category could be “travelsmostly in the afternoon, weekdays” or “sales representative, travelsabove average mileage between cities.” Since those are notcategories/classifications typical of an online ad network (which usemore interest-based rather than route-based categories), the ad networkwould add those new items as new targeting capabilities for itsadvertisers.

In addition, to augment the information at the third party ad networkwith route-based data captured through an embodiment of the disclosemethods and systems, the list of merchants or franchises within areasonable driving distance from the usual routes, from home or usualplace of work is sent at step 950 as potential advertisers of interestfor that driver. This exchange of information to augment the knowledgeof third party advertising networks with route-based information bridgesthe online behavior, with the behavior in the physical world. Thiscomplementary additional profiling data is sent over the link 945 to thepartner ad network from the marketing data provider 930 and can include,for example, the device ID/Advertising ID of the device used by thedriver (which is most likely associated with the ad network cookie ID)and the associated mapped profile in order to expand the reach andsegmentation capabilities of its ad network.

An additional technique can deepen the knowledge of the driver at thepartner ad network level and may be used at step 948 if it is determinedthat the online and physical profiles are significantly different. Inone embodiment, the inference module 948, attempts to inject some of thecharacteristic of the driver's route-based profile into his or heronline profile as determined by third party ad networks. The purpose ofinjecting new characteristics is to slowly correct or influence theonline profile based on what is observed in the physical world. Forexample, an online profile could show a older person constantly shoppingfor medical devices or medication, whereas the physical route-basedprofile could be the profile of a very young person going out often lateat night. The demographics of the two profiles are not consistent andcould be caused by insufficient data collections either online or in thephysical world. If sufficient physical data has already been obtained ona large number of routes confirming the profile with a high degree ofconfidence, the physical profile can influence the online profile, andat step 949, a reverse algorithm can be used to determine, through aseries of steps disclosed below, the set of websites that would need tobe visited online that would result in the third party ad network toattribute a profile closer to the one in the physical world. Step 949would then send those websites to the partner third party ad network sothat they can assume those websites as being visited as part of theonline web browsing experience in order to shift its perception of theonline profile closer to the one determined in the physical world. Thismethod allows the ad network to consider those sites in the future asthe profile of the user is changed over time through new browsingactivity. The resulting cookie (a cookie with its attributes set by bothonline and physical/route-based information) is more optimized forfuture advertising placement. Step 951 ads to this list of websites, thewebsites of the merchants within x minutes detour from routes andmerchants around the areas they spend the most time during certainperiods of the day and/or certain periods of the week. For example,frequent visits to a physical Best Buy establishment can triggerinclusion of bestbuy.com and/or several other electronics stores. As afurther example, a home location determined through route-basedprofiling in a high income neighborhood can trigger the inclusion ofhigh end restaurant chains in the online profile even though pastbrowsing activities may not have detected this interest.

An example embodiment of the disclosed methods and systems can determinethe profitability of a driver, a group of drivers, or a fleet ofself-driven vehicles for various products and services based on routesdriven, rather than traditional parameters. As an example, auto andvehicle insurance companies covering property damage and liability fromaccidents typically use various indirect underwriting factors in orderto determine a driver risk and set corresponding premiums. Theclassification of risks determines the premiums paid to the insurancecompany or the amount of expenses, such as commission expenses, that canbe paid for the same premium. If a risk is in a lower risk class, thenthe insurance company can profitably pay higher commissions or payhigher promotional or advertising expenses to acquire that business.

Unfortunately, indirect underwriting factors are typically given moreimportance than direct factors in evaluating risk. Current actuarialanalysis is based on secondary or indirect underwriting factors, such astype of cars owned, age, zip code, past traffic violations, creditscore, etc. Although these indirect parameters are important as factorsinfluencing risk (and corresponding actuarial and statistical analysiswith a vast amount of historical data points have resulted in positiveprofits to the satisfaction of insurance companies), the primary ordirect factors, such as mileage and speed, which are very difficult tocapture are often ignored in the upfront evaluation of the risk and are,therefore, ignored in the upfront evaluation of the rates and thecommissions paid to insurance agent/brokers. Because they have a muchmore direct influence on the insurance risk, they could be a much betterpredictor of the economics of the insurance cost, especially whencombined with route based interests and demographics.

In some instances, this kind of analysis has been attempted using shortterm vehicle tracking using devices such as the Progressive's Snapshotdevice that plugs into a car's OBD port. The problem with most of thesekinds of devices is that first they are for temporary use as userstypically do not like being permanently tracked for privacy reasons, andsecondly, they are used to get a short snapshot in time of the car anddoes not provide a long term view of the driving habits of a specificdriver. Drivers can be on their best behavior while using those devices,and cars can usually be driven by various family members and, therefore,there could be situations where using a device linked to a vehiclerather than a person does not provide adequate information to underwritethe individual. Another major issue with these types of devices is thatthe information captured is proprietary to a specific insurance carrierand, therefore, cannot be used if the driver wishes to switch insurancecompanies.

Another issue is that with the advent of self-driven cars, the vastamount of historical data points do not exist. Self-driven vehicles areexpected to be safer, but if they are constantly over-ridden byimpatient drivers, their safety benefits will not be apparent. Anothercomplicating factor is that all self-driven cars are not created equal,and some will have a better track record than others on the road interms of accident and profitability for insurance services. Thetraditional parameters (driver age, prior infractions, residence zipcode, car color, etc.) could quickly become irrelevant for assessing theprofitability of this type of risk, especially as more and more hybridcars (cars that can be both self-driven or manually driven) are put inthe market.

To address those obstacles, an example embodiment classifiesprofitability/risk for certain products and services using first partialroute information and then dynamically updating the risk classificationas more route information is added to the driver profile or moredriver-supplied personal traditional information is shared. This methodassumes that the determining risk factor (and therefore profitabilityfactor) is the routes driven and the speed at which those routes aredriven, rather than the traditional information captured throughstandard underwriting or standard segmentation. For example, a risk canbe classified, first, using only driving information without necessarilyknowing the age of the driver for example or his address of residence.This approach can be applied in other industries, such as theprofitability for medical or wireless services being determined based onthe number of routes terminating at medical facilities or pharmacies, asopposed to age or other traditional factors, and the methods and systemscan reclassify the risk once additional information is obtaineddynamically.

Another embodiment can determine the percentage of time a hybrid car isself-driven. Self-driven vehicle software will likely always have thecapability to determine when the car is in self-driven mode or whenthere is an override of its automated functions. It is however highlyunlikely that this information will be shared with insurance carriers ina way that will assist in risk assessments. In one embodiment, thisdetermination is achieved independently by a third party systemconnected by an exchange or clearinghouse with various fleets bycomparing various routes of self-driven vehicles with routes and pathsfrom mobile devices, such as mobile phones carried by drivers. If theroutes taken by the vehicle, as measured by in-vehicle GPS or otherlocation services, are sent to a centralized CPU through an exchange,then the matching of a vehicle ID with a device/driver ID can occur ifthe routes and paths of the device and the car match, and are the samewithin a threshold confidence probability. In that case, the last knownlocation of the car should also match the last known location of thedevice ID, or else it can be determined that the vehicle is a shareddrive, such as, for example, Uber. Furthermore, if the car is overriddentemporarily by a human driver, then matching can continue; however, themileage driven by the human driver can be deducted from the overallmileage for the fleet being driven by the same software and hardwareconfiguration for the purpose of calculating the risk adjusted effectivemileage of the automated driving unit.

The disclosed systems can place the tracking data in the hands ofthird-party intermediaries, rather than in the hands of the providers ofproducts and services. An example system can include an industry wideexchange module that gives product and service providers access to aprofit segmented base of drivers or fleets for promotional purposes, orfor bidding purposes to incentivize the third-party intermediary toconvert or switch a certain profitable portfolio of drivers or book ofbusiness to their brand, and their products.

FIG. 10 illustrates a representation of an environment in whichtraditional vehicles (manually driven 100% of the time) co-exist on theroad with vehicles that are driven manually by humans only some of thetime and with vehicles that are fully automated and self-driven all ofthe time (except for emergency override). FIG. 10 also illustratesmethods and systems to assign a profitability index or risk pool forcertain products and services to a group of drivers or self-drivenvehicles. FIG. 10 also discloses an example attribution of vehicles'movements to specific mobile devices, especially in the case ofself-driven vehicles.

FIG. 10 includes a flowchart representing an algorithm that may beexecuted by software code residing on the centralized system server. Itdiscloses how each driver or each fleet of self-driving vehicles can beassigned to a pool or segment based on their estimated profitability forvarious products and services. In general, the profitability of severalproducts and services can be linked primarily to routes driven (e.g.,how many miles are driven, where, when, and at what speed). Otherfactors, such as how the vehicle is driven (e.g., sudden acceleration,deceleration, or changes of lanes) can also have an effect. The list ofsuch products and services can be quite long when the routes are used todetermine a driver profile, and the profile can be determined based onroutes driven to infer interests, age, and other demographics. Forexample a low-mileage vehicle during weekdays can indicate a retiredperson, but a profile based on routes to entertainment places andairports, as opposed to hospitals and pharmacies, may indicate ahighly-active retiree, and travel packages could be marketed as opposedto medical products. Vehicle insurance to cover property and liabilitylosses is another example of a product or service whose profitabilityoften depends primarily on routes driven factors. Often, profitabilitywithout route information is inferred indirectly using availableinformation. In the case of vehicle insurance, traditional factorsinclude, age, vehicle type, residence zip code, credit score, and pastinfractions, but direct factors, such as how many miles are driven,where, when, and at what speed are not considered as this information isnot typically available. Similarly, for fleets of self-driven cars, themanufacturer of the vehicle software and sensors could be taken intoconsideration, but the primary determinant of how many miles are drivenwithout an accident and on what type of roads (the self-driven software“road experience” and reliability index) could be an important aspect toconsider. As an example for vehicle insurance, two fleets with similarmileage without accident should not be treated equally if one fleet isdriven primarily on highways and the other primarily in urban settingswhere collision avoidance is more challenging and is a better indicationof future profitability.

Embodiments of the disclosed methods and systems may assume that thereis a mix of vehicles being tracked: vehicles driven 100% by a humandriver, vehicles driven from time-to-time by human drivers, andself-driven vehicles driven using on-board software and sensors (exceptin case of emergency manual override). For certain products andservices, it can be important to distinguish if a mobile device beingtracked on roads is linked to an active driver or a passive passenger ina self-driven vehicle. As an example for vehicle insurance, it isdifficult to evaluate profit potential of a driver that manually driveshis vehicle only 30% of the time by a human driver and the rest of thetime uses the vehicle in self-driving mode. The driver may not be liablefor accidents occurring in self-driven mode, and the mileage inself-driven mode is not an indication of the insurance risk of thedriver. In fact it should have an opposite effect since a significantnumber of miles using a self-driven mode from a manufacturer with atrack record of no accidents should actually reduce the risk associatedwith the driver (regardless of his age, residence zip code, creditscore, and other factors used traditionally by insurance carriers todetermine risk). FIG. 10 shows how the example system differentiateswhen a route segment is driven manually or in a self-driven mode. Thefleets of self-driving capable vehicles from the same manufacturer 1021may keep in their logs the geographic coordinates of the vehicle and thetime periods during which the vehicle operated automatically, which canbe sent from time-to-time to the manufacturer. These logs can be sentthrough an exchange interface 1022 to the system's centralized server,which stores the data in a database 1023 accessible by a module thatassigns profitability to drivers.

The self-driven cars capture a large amount of data that can be madeavailable through the exchange interface 1022. Included in that data setmay be the GPS coordinates of the routes taken by the vehicle and theroute segments during which the vehicle is in self-driving mode. Thisdata can be stored in database 1023. Similarly the GPS measurements forthe routes taken by mobile devices of participating users can be storedin database 1024. It is important to understand when self-driven routesegments occur, since liability in case of an accident can shift fromthe driver to the manufacturer of the self-driven vehicle and itssoftware vendors controlling similar fleets of vehicles from differentcar manufacturers. In addition, the mileage during those route segmentsshould not accrue to the driver for the purpose of profitabilityassessments, but rather to the software vendors for vehicles equippedwith similar self-driven software.

In one embodiment, the user of a mobile device 1012 pairs his devicewith the self-driving capable vehicle 1013 using Bluetooth, Wi-Fi,physical cable, RFID, or any other short-range wireless signal. In theexample case, the information sent through the exchange 1022, which isconnected to various self-driven systems and stored in database 1023,already contains this pairing with a specific driver ID and it isrelatively simple to remove from the database 1024 the road segmentsduring which the car was self-driven. In another example case, thepairing does not occur (for example, the user of mobile device 1012 doesnot have his Bluetooth/RFID/Wi-Fi on or is a passenger in a self-drivencar belonging to a fleet of vehicles such as Uber. In this case, it isdifficult to match the route segments in the database 1024, which tracksthe mobile device for a specific driver with the route segments duringwhich the user is not manually driving the car. The route matchingmodule can extract that information, however, by comparing the two setsof data. Since there is no pairing of a vehicle with self-drivingcapabilities with a driver ID, the number of route segments to compareand match between the two databases can be very large. The methodsdescribed herein reduce the computation-intensive matching process tosomething that is manageable and low cost in terms of CPU processingpower required to perform such a complex task.

In order to determine whether a person with a mobile device is manuallydriving a vehicle, a path A from the GPS coordinates captured by thevehicle's self-driven software can be matched with a path B from the GPScoordinates of the mobile device 1012. If they are determined withenough confidence to be along the same path from the same origin pointO1 1015 to the same destination point D2 1017 the mobile device 1012 maybe paired with the vehicle 1013. To do this programmatically, a notionof trip similarity can be defined. If two or more trips are similar,then they can be combined into a route. The system can assess thesimilarity of two trips. The algorithm can relies on thelatitude-longitude data from trips if sufficient data points arecollected. If not, it requires an extra step to inferring which roadswere traversed and can assume that the same route segments have beentaken between two measurements at two road intersections Ia and Ib inorder to reduce GPS measurements.

The similarity module 1050, after identifying trips (Trip A and Trip B)that are in the same vicinity, can use an algorithm to compute anaverage minimum point-segment distance between those two trips (e.g.,from Trip A to Trip B). These averages can be added together and dividedby two to calculate a symmetric score representing the similaritybetween the two trips. An example similarity algorithm may be asfollows:

1. For every point P_(Ai) in Trip A, find the closest trip segmentTS_(Bj) in Trip B. A trip segment is a straight line between twotemporally adjacent GPS points. Calculate the minimum point-segmentdistance between P_(Ai) and TS_(Bj).

2. Add together these point-segment distances to compute the totaldistance between Trip A and Trip B: TotalDistanceAB.

3. Calculate the similarity score (ScoreAB) by dividing TotalDistanceABby the number of data points in Trip A.

The example method compares points in one trip to line segments in theother trip, which helps account for variations in GPS sampling ratebetween the self-driven car GPS system and the location measurements ofthe mobile device. The sampling rate is important since the device, tosave battery, does not calculate GPS measurements between the points Ba1092 and Bb 1093 (GPS is not used once outside of Area a 1090 around theintersection Ia as measured by exiting at point Ba 1092 and untilentering Area b 1091 of high frequency measurements around the nextintersection Ib at point Bb 1093).

If the similarity between the two trips is below a specific threshold,the mobile device 1012 can be considered paired with the self-driven car1013, and once this is determined, the pairing can remain until theself-driven car reaches a destination point. This “stickiness” of thepairing reduces computing processing power since the matching processdescribed above is no longer necessary once the pairing is determinedwith sufficient confidence.

In some cases there could be another device 1011 that has an adjacentpoint of origin (O2 at 1015) and travelling alongside vehicle 1013between point O2 and point B 1018. In this case the mobile device couldbe considered paired until it turns in a different direction at point B1018 along path 1010 towards destination point D1 1014. As a result, thefinal pairing does not occur even if the paths are very similarinitially. When there are multiple mobile devices that could match theknown path of the self-driven vehicle, the final pairing decision canoccur when there the similarities of the two mobile device paths isremoved as one of the mobile device takes a significantly differentroute as illustrated at point B 1018.

The route matching algorithm can also snap routes on map roadscoordinates. Measured points can be snapped to the most likely roads thevehicle was traveling along resulting in a path that smoothly followsthe geometry and connectivity of the road. This can be important sincemost of the data captured and linked to road segments are related to mapbased information (number of car accidents per year on road segment indatabase 1030 for example). The route matching module 1025 may alsoimplements a “snapping” algorithm to reconstruct paths/routes travelledin a real outdoor environment from the mobile device or self-drivenvehicle given an existing map and road data of that environment.Moreover, since the road networks exist across various geospatial datasources (e.g., satellite imagery, demographics maps), the road topologyshould be consistent in its representation. To implement the snappingalgorithm, a traversed path by a vehicle or route segment (constructedusing transmitted data measurement from mobile devices) may be denotedas a sum of segment-angle pairs (S_(k), O_(k)) where the angle O is theangle between two adjacent segments S_(k) and S_(k-1) and a segment S isfrequently identified with its length. Each segment and anglemeasurement can contain some unknown error from the true value (thereference map geospatial data). A route is considered a collection ofsegments specified by its endpoints. When the GPS signal is lost or notused for battery efficiency, the traversed path of the mobile device canbe extracted from the accelerometer and gyroscope signals of the devicein the form of length heading pair along with cell ID or enhanced cellID with time advance techniques. The snapping algorithm to a referencetopological map route can find all sequences of segments in thetopological map that match with measured trajectory segments of thedevice or self-driven car within the given error thresholds. In order tosearch all possibilities, the algorithm can uses a recursive function.If there are several drivers/devices tracked on the same route, thealgorithm may select from several trajectories produced and output theone with a smallest summed error as a final result determination of amatch. If there is no definitive selection, one can lower the thresholdand rerun the algorithm until a trajectory is successfully snapped tothe topological route on a map. The map snapping module may then reportan error rate, which can be proportional to the distance between thereceived and snapped coordinates. Once the route matching and routesnapping is completed 1025 it is determined if the vehicle is partiallyself-driven step 1026 and the percentage of time and route segmentswhere it is self-driven 1028.

In some embodiments, classifying the driver into one or more groups caninclude classifying a group of drivers using partially self-drivenvehicles for the purpose of predicting an insurance risk according toinformation not made available to third parties by the providers ofself-driven systems and self-driven vehicles. In addition to not pairingthe driver ID to the vehicle, the self-driven system may not provide keyinformation regarding when and where the vehicle is being self-driven.In addition, it may not provide the number or density of self-drivenvehicles on the routes being driven. This information set can have animportant impact on the insurance risk of the vehicle and of the driver.In case of an accident, the manufacturer of the self-driven system maybe liable if the vehicle was being self-driven at the time of theaccident, so it is very likely that such information will reside in thelogs of the self-driving system manufacturer, but it is unlikely thatthe data will be shared with the parties that are evaluating theinsurance risk when there are no accidents. This information, however ,can be important in ascertaining several factors that have an impact onthe insurance risks of the driver and the vehicle, assuming that aself-driven vehicle on average will present a much lower risk than amanually-driven vehicle in the same way that airplanes on auto-pilot areon average safer than planes manually controlled by pilots, who can fallasleep or forget to check their instruments. Similarly, a high densityor a high number of surrounding self-driven vehicles can reduce the riskassociated with the vehicle, especially as manufacturers standardizetheir interfaces and interconnect vehicles in such a way thatself-driven systems are aware of the path and trajectory of surroundingvehicles without having to rely on sensors, as this information can beprovided by communicating data between vehicles. For those reasons, theinsurance risk will be deemed lower when the vehicle is driven on routesegments with a high percentage of other vehicles that are alsoself-driven and when the vehicle is in general on self-driven mode.Finally, where the vehicle is driven has an impact as well. In the sameway that a plane is usually manually controlled during take-off andlanding, there are some areas where vehicles being manually controlledwill be safer, such as in urban areas. To better assess the insurancerisk, it can be important to know the percentage of time the vehicle isin a self-driven mode, the type of route segments being driven while inself-driven mode, and the speed and mileage driven while in self-drivenmode. In such embodiments, if the information is not made available bythe self-driven system manufacturers through the exchange interface1022, the classification is achieved by, first, matching a specificdriver to a self-driven vehicle by matching the path of the driver'smobile device to the path of the self-driven vehicle, and then thedetermination of when and where the vehicle is in self-driven mode isfurther achieved by matching the typical driving pattern or “signature”of such self-driven vehicle with the encoded driving information. Thematching process can include comparing the encoded driving informationcaptured from the mobile device of the driver with (i) typical speed ofself-driven vehicles over specific route segments and (ii) typicalacceleration and deceleration patterns of self-driven vehicles in thecontext of traffic lights or changing lanes. Once the driving patternmatching is completed, the extent to which the vehicle is beingself-driven can be determined and the corresponding encoded drivinginformation associated to the underlying driving technology, and theextent to which the vehicle is driven manually can be determined and thecorresponding encoded driving information associated to the properdriver ID.

The route segment and the time periods during which the vehicle isself-driven may be analyzed for profitability determination separatelyin 1029 and 1027 since the factors determining profitability impacts arevery different. A database of parameters in 1039 and 1035 may be used todetermine the profitability of the driver or a fleet of vehicles forcertain products and services. The factors in 1039 can include theprofile of the driver based on the historical routes.

The following are examples of selected parameters 1039 that can be usedto classify drivers based on routes for different products and services:

How the vehicle is driven:

How many miles were driven—For certain products, mileage is adetermining factor. For vehicle insurance, more miles driven means thatthe vehicle is often put at risk of causing property or liabilityclaims.

How much time on the road—For certain products, time on the road is adetermining factor. For data and bandwidth usage on outdoors macrowireless networks, in vehicle use of music streaming or podcasts aremajor contributors of data consumption and profitability.

How fast the vehicle is moving—For certain products, speed is adetermining factor, especially when combined with time on the road andmileage when compared to types of roads. For example for vehicleinsurance, high speed on highways for long periods of time is not asrisky as high speed for short period of time in low speed limits urbanzones. Speed and acceleration can also determine the number of timesdangerous maneuvers occurred: speed above speed limits by a certainthreshold or acceleration/deceleration (braking) considered too suddenaccording to a certain threshold.

How continuous is the driving—Number of stops can be a determiningfactor for some products and to determine if the vehicle is being usedfor commercial purposes (delivery or shared ride) or for leisure.

Where is the vehicle driven:

Demographics of areas traversed—Characteristics of area traversed can beimportant, especially the density of certain types of merchants todetermine a profile: is the driver using vehicle mostly in commercialareas or residential areas. Is he driving mostly around malls, medicalcenters, or visiting clients?

Demographics and type of zoning to classify points of origin anddestination points—Point of origin and destination can be important todetermine a profile of the driver and his or her profitability potentialfor certain products and service. If, for example, points of origin arevery high income areas with destinations to restaurants and luxurystores during the day, this implies someone with high disposable incomefor several products and services.

Type of road and characteristics—Characteristics of the roads, such ashistorical number of accidents per day or speed limits, have adetermining effect on profitability. If, for example, a driver takes ahighway with a higher than average number of accidents per day,profitability for vehicle insurance is affected. The traditional riskfor car accidents on those roads can also be based on, for example, (a)time of day, based on historical frequency and severity of claims onthose road segments at specific time of day, (b) road conditions asestimated by weather conditions during specific dates, and (c) thepercentage of self-driven cars on those road segments versus humandrivers (e.g., assumption that automated cars will cause lessaccidents).

When the vehicle is driven:

Time of day—Time of day can be an important factor to determine aprofile, along with destination points. A vehicle travelling toentertainment areas around 1 AM can indicate a young individual that mayget inebriated, and affects profitability potential for vehicleinsurance or adventure travel packages, such as cruises.

Frequency—route frequency reinforces inferred behavior and profileconclusions and helps determine home, work, and other key areas, such asarea most likely to go to shop during weekends. This has a generaldetermining effect on profitability for products sold in the adjacentmalls, for example.

The following are examples of selected parameters 1039 that can be usedto classify fleets of self-driven vehicles based on vehiclecharacteristics and underlying technology:

Software version and computational power residing on vehicle—Softwareversion can be important. The same car that may not have been updatedwith the latest software may be more prone to accidents.

Hardware Type and Version of Subsystems

GPS configuration and location services—The primary subsystem used fornavigation and guidance can be based on a GPS (Global PositioningSystem) receiver, which computes present position based on complexanalysis of signals received from at least four of the constellation ofover 60 low-orbit satellites. GPS systems configuration can beclassified in terms of accuracy sensitivity and response time to firstfix. GPS can provide location accuracy on the order of one meter (theactual number depends on many subtle issues), which is a good start forthe vehicle. Note that for a driver, who hopes to enter the car and getgoing, a GPS receiver takes between 30 and 60 seconds to establishinitial position (time to first fix), so the autonomous vehicle mustdelay its departure until this first fix is computed. GPS subsystems arenow available as sophisticated system on a chip (SoC) IC or multi-chipchipsets which require only power and antenna, and include an embedded,application-specific compute engine to perform the intensivecalculations. Although many of these ICs have an internal RF preamp forthe 1.5-GHz GPS signal, many of the vehicles opt to put the antenna onthe roof with a co-located low-noise amplifier (LNA) RF preamplifier,and locate the GPS circuitry in a more convenient location within thevehicle.

Camera/LIDAR/Radar configuration—To enable split-second decision-makingneeded for self-driving cars, the LIDAR system (light detection andranging, or a combination of light and radar subsystems) providesaccurate 3D information on the surrounding environment. Using this data,the processor implements object identification, motion vectordetermination, collision prediction, and avoidance strategies. Forradars built into the front and rear bumpers and sides of the vehicle,factors may take into consideration the operating frequency for theradar which is usually 77 GHz, to estimate the RF propagationcharacteristics, and sufficiency of resolution.

On board processing power—The processing power of the vehicle can beimportant to determine the speed at which the vehicle can executecomplex software and process complex images and measurements fromvarious sensor to make split second driving decisions.

Other Sensor Array configuration and sensitivity—Gyroscopes,accelerometers, proximity sensors, compass, sextant, and dead reckoning,for example.

Networking capabilities—Extent to which the self-driven cars arenetworked together so that decisions are made in a coordinated way withdecision made by other nearby self-driven cars.

The set of data repository 1039 can therefore associates to each paireddriver ID/device ID a primary set of data r₁ to r_(n) for the routes andi_(l) to i_(n) for the derived driver interest. Similarly forself-driven vehicles, a primary set of data for paired driverID/self-driven car ID if parameters SD₁ to SD_(N) representing theself-driven vehicles characteristics and capabilities as describedabove.

Data set 1035 provides the weights or coefficients for various productsand services that are used to determine profitability. In oneembodiment, there is a set of weights or coefficients for vehicle ordriver or fleet insurance. In another embodiment the set is aimed atpredicting wireless communication services profitability. In anotherembodiment, a set of coefficients is used for medical devices andservices profitability. The process to determine profitability forvarious services using route information are similar: First, speed andcumulative mileage per time period are used at step 1032 as determiningfactors. Then the profitability can be revised for types of roadtravelled (e.g., surrounding area demographics, historical accidentstatistics) and time of day on those roads to adjust the mileage andspeed parameters with the impact that those two factors have onprofitability. Then the profitability can be revised further using theinferred profile of the driver from historical routes 1039 or frominformation provided by the driver using the application on the device1049. In the case of a self-driven vehicle, the route segments beingself-driven can be adjusted using the characteristics/profile of theself-driven car instead of the driver profile (e.g., software version,sensor configuration, and sensitivity, etc.) 1039.

A “base case” that estimates the profitability based on traditionalfactors, rather than routes, is entered in 1035 based on prior steps inmodule 1030 using, for example, tables traditionally used byunderwriters or marketing professionals familiar in the art ofpredicting profitability based on data segmentation. Those base tablescan be modified to provide more importance on primary factors that werepreviously unavailable for the purpose of creating such estimates. Basecoefficients are then determined to assess the relationship ofindependent variables provided by the system to generate the estimatedprofitability in the base tables. The algorithm then uses thosecoefficients to weight in real-time the various variables obtainedthrough the analysis of routes travelled and profile category indices.

Using a feedback loop 1037 provides actual profitability from theservice providers for certain drivers or fleet and a comparison is madeat step 1038. Using those actual values for a sample of drivers, thesoftware causes the processor to first assess each variable separatelyfirst (e.g., obtaining measures of central tendency and dispersion,frequency distributions, etc.) to assessing to what extent the variableis normally distributed and assess the relationship of each independentvariable, one at a time, with the dependent profitability variable(calculating the correlation coefficient) and assessing therelationships between all of the independent variables with each other(obtaining a correlation coefficient matrix for all the independentvariables) and assessing the extent to which the independent variablesare correlated with one another. The feedback loop 1037 and thetraditional factors captured through the application 1049 and estimatesof profitability using traditional factors 1036 are used to adjust thecoefficients in database 1035 in order to have a multiple regressionusing a least squares methods to have a profitability predictor that isadjusted for actual results. Over time, the method using actual routesand route characteristics to determine profitability provides betterresults. For example fleet A from Manufacturer A and fleet B frommanufacturer B can both have similar loss ratios if traditional methodswere used, but the root cause of this similarity could be verydifferent, something that traditional methods cannot distinguish: fleetB could have low claims because fleet B is driven mainly in rural areas,whereas fleet A can have low claims because it has excellent softwareeven when driven mostly in urban areas. This distinction cannot be madewithout capturing the route characteristics and including them asfactors in the calculation of profitability. The risk adjusted systemwill be such that risk pool will be determined as a factor of (1) howmuch the car is driven (risk adjusted mileage adjusted for where it isdriven), (2) how the car was driven, (3) the cost of potential claim,and (4) who/what is driving the car. The risk pool assessment usesdifferent weights to those four factors and put them in pools or groupsthat are predictors of the future insurance cost (loss ratio) andintermediary cost of the individuals or fleets in the pool. The weightsused in the various regression analyses to determine classifications canbe altered using the feedback loop of actual claims until the revisedweights when applied retroactively to historical data can be a betterpredictor of the claims as provided by the feedback loop.

While this invention has been particularly shown and described withreferences to example embodiments thereof, it will be understood bythose skilled in the art that various changes in form and details may bemade therein without departing from the scope of the inventionencompassed by the appended claims.

What is claimed is:
 1. A method of collecting driving information andclassifying drivers, including human drivers or self-driving systems,the method comprising: collecting driving information using a deviceassociated with a driver and a vehicle, the driving informationincluding routes driven in the vehicle, geocoded locations, mileage,times of day, and speeds, the collection of driving information beingenabled and disabled based on location and movement of the device toreduce use of power and bandwidth; encoding the driving information andtransmitting the encoded driving information to a server; storing, in adatabase associated with the server, an identifier associated with thedriver and the encoded driving information; determining, and storing inthe database, predicted future typical route segments that the driver islikely to travel over a certain period of time and associated times ofday based on the encoded driving information; and classifying the driverinto one or more groups based on the encoded driving information.
 2. Asystem for collecting driving information and classifying drivers,including human drivers or self-driving systems, the system comprising:a device associated with a driver and a vehicle and configured to (i)collect driving information, the driving information including routesdriven in the vehicle, geocoded locations, mileage, times of day, andspeeds, (ii) enable and disable the collection of driving informationbased on location and movement of the device to reduce use of power andbandwidth, (iii) encode the driving information, and (iv) transmit theencoded driving information to a server; a database associated with theserver, in communication with the device, and storing an identifierassociated with the driver, the encoded driving information, andpredicted future typical route segments that the driver is likely totravel over a certain period of time and associated times of day basedon the encoded driving information; and a processor associated with theserver, in communication with the database and the device, andconfigured to determine the predicted future typical route segments andclassify the driver into one or more groups based on the encoded drivinginformation.